Search results for: Subspace clustering

Authors: Shivakumar, G. S. Vijay, P. Srinivas Pai, B. R. Shrinivasa Rao

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In the present study, RBF neural networks were used for predicting the performance and emission parameters of a biodiesel engine. Engine experiments were carried out in a 4 stroke diesel engine using blends of diesel and Honge methyl ester as the fuel. Performance parameters like BTE, BSEC, Tex and emissions from the engine were measured. These experimental results were used for ANN modeling. RBF center initialization was done by random selection and by using Clustered techniques. Network was trained by using fixed and varying widths for the RBF units. It was observed that RBF results were having a good agreement with the experimental results. Networks trained by using clustering technique gave better results than using random selection of centers in terms of reduced MRE and increased prediction accuracy. The average MRE for the performance parameters was 3.25% with the prediction accuracy of 98% and for emissions it was 10.4% with a prediction accuracy of 80%.

Keywords: Radial Basis Function networks, emissions, Performance parameters, Fuzzy c means.

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Authors: Khalid Ghoulam, Belaid Bouikhalene, Zakaria Harmouch, Hicham Mouncif

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One of the essential topics in the information systems is the registration management. The objective of this project is to create a web portal designed to help new students on the first access to the Sultan Moulay Slimane University SMSU (Practical Information, Pre-Registration, Placement Test, Terms of use ... etc.) while creating a secure space protecting both data from the institutions of the University and student information. This portal is accessible from any computer connected to the Internet inside and outside the campus. In this work, we present a platform on the first access to the SMSU which is essential for authentication in the digital work space of the university. This platform allows university to make better decisions for students clustering, to avoid traditional manual method, and to reduce the cost in human and material resources.

Keywords: Registration, SMSU, Security, FAUSMS, digital work space, Placement test.

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Authors: Nhien-An Le-Khac, Sammer Markos, M-Tahar Kechadi

Abstract:

Today, money laundering (ML) poses a serious threat not only to financial institutions but also to the nation. This criminal activity is becoming more and more sophisticated and seems to have moved from the cliché of drug trafficking to financing terrorism and surely not forgetting personal gain. Most international financial institutions have been implementing anti-money laundering solutions (AML) to fight investment fraud. However, traditional investigative techniques consume numerous man-hours. Recently, data mining approaches have been developed and are considered as well-suited techniques for detecting ML activities. Within the scope of a collaboration project for the purpose of developing a new solution for the AML Units in an international investment bank, we proposed a data mining-based solution for AML. In this paper, we present a heuristics approach to improve the performance for this solution. We also show some preliminary results associated with this method on analysing transaction datasets.

Keywords: data mining, anti money laundering, clustering, heuristics.

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Authors: Elham Alaee, Mousa Shamsi, Hossein Ahmadi, Soroosh Nazem, Mohammadhossein Sedaaghi

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Human face has a fundamental role in the appearance of individuals. So the importance of facial surgeries is undeniable. Thus, there is a need for the appropriate and accurate facial skin segmentation in order to extract different features. Since Fuzzy CMeans (FCM) clustering algorithm doesn’t work appropriately for noisy images and outliers, in this paper we exploit Possibilistic CMeans (PCM) algorithm in order to segment the facial skin. For this purpose, first, we convert facial images from RGB to YCbCr color space. To evaluate performance of the proposed algorithm, the database of Sahand University of Technology, Tabriz, Iran was used. In order to have a better understanding from the proposed algorithm; FCM and Expectation-Maximization (EM) algorithms are also used for facial skin segmentation. The proposed method shows better results than the other segmentation methods. Results include misclassification error (0.032) and the region’s area error (0.045) for the proposed algorithm.

Keywords: Facial image, segmentation, PCM, FCM, skin error, facial surgery.

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Authors: R. Saraçoğlu, N. Allahverdi

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Searching similar documents and document management subjects have important place in text mining. One of the most important parts of similar document research studies is the process of classifying or clustering the documents. In this study, a similar document search approach that includes discussion of out the case of belonging to multiple categories (multiple categories problem) has been carried. The proposed method that based on Fuzzy Similarity Classification (FSC) has been compared with Rocchio algorithm and naive Bayes method which are widely used in text mining. Empirical results show that the proposed method is quite successful and can be applied effectively. For the second stage, multiple categories vector method based on information of categories regarding to frequency of being seen together has been used. Empirical results show that achievement is increased almost two times, when proposed method is compared with classical approach.

Keywords: Document similarity, Fuzzy classification, Multiple categories, Text mining.

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Authors: Cheng-Lung Huang, Cheng-Wei Lin

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This study proposes a new recommender system based on the collaborative folksonomy. The purpose of the proposed system is to recommend Internet resources (such as books, articles, documents, pictures, audio and video) to users. The proposed method includes four steps: creating the user profile based on the tags, grouping the similar users into clusters using an agglomerative hierarchical clustering, finding similar resources based on the user-s past collections by using content-based filtering, and recommending similar items to the target user. This study examines the system-s performance for the dataset collected from “del.icio.us," which is a famous social bookmarking website. Experimental results show that the proposed tag-based collaborative and content-based filtering hybridized recommender system is promising and effectiveness in the folksonomy-based bookmarking website.

Keywords: Collaborative recommendation, Folksonomy, Social tagging

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Authors: Zhongjie Wang, Xiaofei Xu, Dechen Zhan

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With deep development of software reuse, componentrelated technologies have been widely applied in the development of large-scale complex applications. Component identification (CI) is one of the primary research problems in software reuse, by analyzing domain business models to get a set of business components with high reuse value and good reuse performance to support effective reuse. Based on the concept and classification of CI, its technical stack is briefly discussed from four views, i.e., form of input business models, identification goals, identification strategies, and identification process. Then various CI methods presented in literatures are classified into four types, i.e., domain analysis based methods, cohesion-coupling based clustering methods, CRUD matrix based methods, and other methods, with the comparisons between these methods for their advantages and disadvantages. Additionally, some insufficiencies of study on CI are discussed, and the causes are explained subsequently. Finally, it is concluded with some significantly promising tendency about research on this problem.

Keywords: Business component, component granularity, component identification, reuse performance.

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Authors: T.P. Sharma

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Querying a data source and routing data towards sink becomes a serious challenge in static wireless sensor networks if sink and/or data source are mobile. Many a times the event to be observed either moves or spreads across wide area making maintenance of continuous path between source and sink a challenge. Also, sink can move while query is being issued or data is on its way towards sink. In this paper, we extend our already proposed Grid Based Data Dissemination (GBDD) scheme which is a virtual grid based topology management scheme restricting impact of movement of sink(s) and event(s) to some specific cells of a grid. This obviates the need for frequent path modifications and hence maintains continuous flow of data while minimizing the network energy consumptions. Simulation experiments show significant improvements in network energy savings and average packet delay for a packet to reach at sink.

Keywords: Mobility in WSNs, virtual grid, GBDD, clustering.

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Authors: Wernhuar Tarng, Kun-Jie Huang, Li-Zhong Deng, Kun-Rong Hsie, Mingteh Chen

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In recent years, the research in wireless sensor network has increased steadily, and many studies were focusing on reducing energy consumption of sensor nodes to extend their lifetimes. In this paper, the issue of energy consumption is investigated and two adaptive mechanisms are proposed to extend the network lifetime. This study uses high-energy-first scheme to determine cluster heads for data transmission. Thus, energy consumption in each cluster is balanced and network lifetime can be extended. In addition, this study uses cluster merging and dynamic routing mechanisms to further reduce energy consumption during data transmission. The simulation results show that the proposed method can effectively extend the lifetime of wireless sensor network, and it is suitable for different base station locations.

Keywords: Wireless sensor network, high-energy-first scheme, adaptive mechanisms, network lifetime

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Authors: K.Mala, V.Sadasivam, S.Alagappan

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Advances in clinical medical imaging have brought about the routine production of vast numbers of medical images that need to be analyzed. As a result an enormous amount of computer vision research effort has been targeted at achieving automated medical image analysis. Computed Tomography (CT) is highly accurate for diagnosing liver tumors. This study aimed to evaluate the potential role of the wavelet and the neural network in the differential diagnosis of liver tumors in CT images. The tumors considered in this study are hepatocellular carcinoma, cholangio carcinoma, hemangeoma and hepatoadenoma. Each suspicious tumor region was automatically extracted from the CT abdominal images and the textural information obtained was used to train the Probabilistic Neural Network (PNN) to classify the tumors. Results obtained were evaluated with the help of radiologists. The system differentiates the tumor with relatively high accuracy and is therefore clinically useful.

Keywords: Fuzzy c means clustering, texture analysis, probabilistic neural network, LVQ neural network.

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Authors: S. Ben Chaabane, M. Sayadi, F. Fnaiech, E. Brassart

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In this paper we propose a new knowledge model using the Dempster-Shafer-s evidence theory for image segmentation and fusion. The proposed method is composed essentially of two steps. First, mass distributions in Dempster-Shafer theory are obtained from the membership degrees of each pixel covering the three image components (R, G and B). Each membership-s degree is determined by applying Fuzzy C-Means (FCM) clustering to the gray levels of the three images. Second, the fusion process consists in defining three discernment frames which are associated with the three images to be fused, and then combining them to form a new frame of discernment. The strategy used to define mass distributions in the combined framework is discussed in detail. The proposed fusion method is illustrated in the context of image segmentation. Experimental investigations and comparative studies with the other previous methods are carried out showing thus the robustness and superiority of the proposed method in terms of image segmentation.

Keywords: Fuzzy C-means, Color image, data fusion, Dempster-Shafer's evidence theory

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Authors: Jia-Hong Lee, Mei-Yi Wu, Hsien-Tsung Kuo

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Texture is an important characteristic in real and synthetic scenes. Texture analysis plays a critical role in inspecting surfaces and provides important techniques in a variety of applications. Although several descriptors have been presented to extract texture features, the development of object recognition is still a difficult task due to the complex aspects of texture. Recently, many robust and scaling-invariant image features such as SIFT, SURF and ORB have been successfully used in image retrieval and object recognition. In this paper, we have tried to compare the performance for texture classification using these feature descriptors with k-means clustering. Different classifiers including K-NN, Naive Bayes, Back Propagation Neural Network , Decision Tree and Kstar were applied in three texture image sets － UIUCTex, KTH-TIPS and Brodatz, respectively. Experimental results reveal SIFTS as the best average accuracy rate holder in UIUCTex, KTH-TIPS and SURF is advantaged in Brodatz texture set. BP neuro network works best in the test set classification among all used classifiers.

Keywords: Texture classification, texture descriptor, SIFT, SURF, ORB.

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Authors: Mahmood Reza Shakarami, Reza Sedaghati

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Recently, distributed generation technologies have received much attention for the potential energy savings and reliability assurances that might be achieved as a result of their widespread adoption. The distribution feeder reconfiguration (DFR) is one of the most important control schemes in the distribution networks, which can be affected by DGs. This paper presents a new approach to DFR at the distribution networks considering wind turbines. The main objective of the DFR is to minimize the deviation of the bus voltage. Since the DFR is a nonlinear optimization problem, we apply the Adaptive Modified Firefly Optimization (AMFO) approach to solve it. As a result of the conflicting behavior of the single- objective function, a fuzzy based clustering technique is employed to reach the set of optimal solutions called Pareto solutions. The approach is tested on the IEEE 32-bus standard test system.

Keywords: Adaptive Modified Firefly Optimization (AMFO), Pareto solutions, feeder reconfiguration, wind turbines, bus voltage.

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Authors: Ronit Chakraborty, Sugata Banerji

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There has been a marked increase in the reported prevalence of Autism Spectrum Disorder (ASD) among children in the US over the past two decades. This research has analyzed the growth in state-level ASD prevalence against 45 different potentially explanatory factors including socio-economic, demographic, healthcare, public policy and political factors. The goal was to understand if these factors have adequate predictive power in modeling the differential growth in ASD prevalence across various states, and, if they do, which factors are the most influential. The key findings of this study include (1) there is a confirmation that the chosen feature set has considerable power in predicting the growth in ASD prevalence, (2) the most influential predictive factors are identified, (3) given the nature of the most influential predictive variables, an indication that a considerable portion of the reported ASD prevalence differentials across states could be attributable to over and under diagnosis, and (4) Florida is identified as a key outlier state pointing to a potential under-diagnosis of ASD.

Keywords: Autism Spectrum Disorder, ASD, clustering, Machine Learning, predictive modeling.

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Authors: Diego Felix, Eugene Hyun, Michael McGuire, Mihai Sima

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Terminal localization for indoor Wireless Local Area Networks (WLANs) is critical for the deployment of location-aware computing inside of buildings. A major challenge is obtaining high localization accuracy in presence of fluctuations of the received signal strength (RSS) measurements caused by multipath fading. This paper focuses on reducing the effect of the distance-varying noise by spatial filtering of the measured RSS. Two different survey point geometries are tested with the noise reduction technique: survey points arranged in sets of clusters and survey points uniformly distributed over the network area. The results show that the location accuracy improves by 16% when the filter is used and by 18% when the filter is applied to a clustered survey set as opposed to a straight-line survey set. The estimated locations are within 2 m of the true location, which indicates that clustering the survey points provides better localization accuracy due to superior noise removal.

Keywords: Position measurement, Wireless LAN, Radio navigation, Filtering

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Authors: A. Ouanane, A. Serir

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In this paper, a novel algorithm based on Ridgelet Transform and support vector machine is proposed for human action recognition. The Ridgelet transform is a directional multi-resolution transform and it is more suitable for describing the human action by performing its directional information to form spatial features vectors. The dynamic transition between the spatial features is carried out using both the Principal Component Analysis and clustering algorithm K-means. First, the Principal Component Analysis is used to reduce the dimensionality of the obtained vectors. Then, the kmeans algorithm is then used to perform the obtained vectors to form the spatio-temporal pattern, called set-of-labels, according to given periodicity of human action. Finally, a Support Machine classifier is used to discriminate between the different human actions. Different tests are conducted on popular Datasets, such as Weizmann and KTH. The obtained results show that the proposed method provides more significant accuracy rate and it drives more robustness in very challenging situations such as lighting changes, scaling and dynamic environment

Keywords: Human action, Ridgelet Transform, PCA, K-means, SVM.

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Authors: JiEun Jeong, Se-Won Kang, Hee-Ju Hwang, Sung-Hwa Chae, Sang-Haeng Choi, Hong-SeogPark, YeonSoo Han, Bok-Reul Lee, Dae-Hyun Seog, Yong Seok Lee

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Tofurther advance research on immune-related genes from T. molitor, we constructed acDNA library and analyzed expressed sequence taq (EST) sequences from 1,056 clones. After removing vector sequence and quality checkingthrough thePhred program (trim_alt 0.05 (P-score>20), 1039 sequences were generated. The average length of insert was 792 bp. In addition, we identified 162 clusters, 167 contigs and 391 contigs after clustering and assembling process using a TGICL package. EST sequences were searchedagainst NCBI nr database by local BLAST (blastx, EKeywords: EST, Innate immunity, Tenebriomolitor

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Authors: Mirjana Radman-Funarić, Katarina Potnik Galić

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The paper presents an analysis of linkages and structures of co-operation and their intensity like the potential for the establishment of clusters in the Central and Eastern (Pannonian) Croatian. Starting from the theoretical elaboration of the need for entrepreneurs to organize through the cluster model and the terms of their self-actualization, related to the importance of traditional values in terms of benefits, social capital and assess where the company now is, in order to prove the need to create their own identity in terms of clustering. The institutional dimensions of social capital where the public sector has the best role in creating the social structure of clusters, and social dimensions of social capital in terms of trust, cooperation and networking will be analyzed to what extent the trust and coherency are present between companies in the Brod posavina and Pozega slavonia County, expressed through the readiness of inclusion in clusters in the NUTS II region - Central and Eastern (Pannonian) Croatia, as a homogeneous economic entity, with emphasis on limiting factors that stand in the way of greater competitiveness.

Keywords: Analysis of linkages, structures of co-operation, Cluster, Region

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Authors: Francesco Maiorana

Abstract:

In this paper a one-dimension Self Organizing Map algorithm (SOM) to perform feature selection is presented. The algorithm is based on a first classification of the input dataset on a similarity space. From this classification for each class a set of positive and negative features is computed. This set of features is selected as result of the procedure. The procedure is evaluated on an in-house dataset from a Knowledge Discovery from Text (KDT) application and on a set of publicly available datasets used in international feature selection competitions. These datasets come from KDT applications, drug discovery as well as other applications. The knowledge of the correct classification available for the training and validation datasets is used to optimize the parameters for positive and negative feature extractions. The process becomes feasible for large and sparse datasets, as the ones obtained in KDT applications, by using both compression techniques to store the similarity matrix and speed up techniques of the Kohonen algorithm that take advantage of the sparsity of the input matrix. These improvements make it feasible, by using the grid, the application of the methodology to massive datasets.

Keywords: Clustering algorithm, Data mining, Feature selection, Grid, Kohonen Self Organizing Map.

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Authors: A. Allirani, M. Suganthi

Abstract:

Data gathering is an essential operation in wireless sensor network applications. So it requires energy efficiency techniques to increase the lifetime of the network. Similarly, clustering is also an effective technique to improve the energy efficiency and network lifetime of wireless sensor networks. In this paper, an energy efficient cluster formation protocol is proposed with the objective of achieving low energy dissipation and latency without sacrificing application specific quality. The objective is achieved by applying randomized, adaptive, self-configuring cluster formation and localized control for data transfers. It involves application - specific data processing, such as data aggregation or compression. The cluster formation algorithm allows each node to make independent decisions, so as to generate good clusters as the end. Simulation results show that the proposed protocol utilizes minimum energy and latency for cluster formation, there by reducing the overhead of the protocol.

Keywords: Sensor networks, Low latency, Energy sorting protocol, data processing, Cluster formation.

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Authors: Abdelouahab Moussaoui, Nabila Ferahta, Victor Chen

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The development of aid's systems for the medical diagnosis is not easy thing because of presence of inhomogeneities in the MRI, the variability of the data from a sequence to the other as well as of other different source distortions that accentuate this difficulty. A new automatic, contextual, adaptive and robust segmentation procedure by MRI brain tissue classification is described in this article. A first phase consists in estimating the density of probability of the data by the Parzen-Rozenblatt method. The classification procedure is completely automatic and doesn't make any assumptions nor on the clusters number nor on the prototypes of these clusters since these last are detected in an automatic manner by an operator of mathematical morphology called skeleton by influence zones detection (SKIZ). The problem of initialization of the prototypes as well as their number is transformed in an optimization problem; in more the procedure is adaptive since it takes in consideration the contextual information presents in every voxel by an adaptive and robust non parametric model by the Markov fields (MF). The number of bad classifications is reduced by the use of the criteria of MPM minimization (Maximum Posterior Marginal).

Keywords: Clustering, Automatic Classification, SKIZ, MarkovFields, Image segmentation, Maximum Posterior Marginal (MPM).

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Authors: E. N. Sathishkumar, K. Thangavel, T. Chandrasekhar

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Feature selection is a process to select features which are more informative. It is one of the important steps in knowledge discovery. The problem is that all genes are not important in gene expression data. Some of the genes may be redundant, and others may be irrelevant and noisy. Here a novel approach is proposed Hybrid K-Mean-Quick Reduct (KMQR) algorithm for gene selection from gene expression data. In this study, the entire dataset is divided into clusters by applying K-Means algorithm. Each cluster contains similar genes. The high class discriminated genes has been selected based on their degree of dependence by applying Quick Reduct algorithm to all the clusters. Average Correlation Value (ACV) is calculated for the high class discriminated genes. The clusters which have the ACV value as 1 is determined as significant clusters, whose classification accuracy will be equal or high when comparing to the accuracy of the entire dataset. The proposed algorithm is evaluated using WEKA classifiers and compared. The proposed work shows that the high classification accuracy.

Keywords: Clustering, Gene Selection, K-Mean-Quick Reduct, Rough Sets.

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Authors: J. Mehena, M. C. Adhikary

Abstract:

In this paper, a spatial multiple-kernel fuzzy C-means (SMKFCM) algorithm is introduced for segmentation problem. A linear combination of multiples kernels with spatial information is used in the kernel FCM (KFCM) and the updating rules for the linear coefficients of the composite kernels are derived as well. Fuzzy cmeans (FCM) based techniques have been widely used in medical image segmentation problem due to their simplicity and fast convergence. The proposed SMKFCM algorithm provides us a new flexible vehicle to fuse different pixel information in medical image segmentation and detection of MR images. To evaluate the robustness of the proposed segmentation algorithm in noisy environment, we add noise in medical brain tumor MR images and calculated the success rate and segmentation accuracy. From the experimental results it is clear that the proposed algorithm has better performance than those of other FCM based techniques for noisy medical MR images.

Keywords: Clustering, fuzzy C-means, image segmentation, MR images, multiple kernels.

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Authors: Junjie Gao, Guishi Deng

Abstract:

In order to integrate knowledge in heterogeneous case-based reasoning (CBR) systems, ontology-based CBR system has become a hot topic. To solve the facing problems of ontology-based CBR system, for example, its architecture is nonstandard, reusing knowledge in legacy CBR is deficient, ontology construction is difficult, etc, we propose a novel approach for semi-automatically construct ontology-based CBR system whose architecture is based on two-layer ontology. Domain knowledge implied in legacy case bases can be mapped from relational database schema and knowledge items to relevant OWL local ontology automatically by a mapping algorithm with low time-complexity. By concept clustering based on formal concept analysis, computing concept equation measure and concept inclusion measure, some suggestions about enriching or amending concept hierarchy of OWL local ontologies are made automatically that can aid designers to achieve semi-automatic construction of OWL domain ontology. Validation of the approach is done by an application example.

Keywords: OWL ontology, Case-based Reasoning, FormalConcept Analysis, Knowledge Integration

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Authors: M.V Rajesh, Archana R, A Unnikrishnan, R Gopikakumari, Jeevamma Jacob

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The System Identification problem looks for a suitably parameterized model, representing a given process. The parameters of the model are adjusted to optimize a performance function based on error between the given process output and identified process output. The linear system identification field is well established with many classical approaches whereas most of those methods cannot be applied for nonlinear systems. The problem becomes tougher if the system is completely unknown with only the output time series is available. It has been reported that the capability of Artificial Neural Network to approximate all linear and nonlinear input-output maps makes it predominantly suitable for the identification of nonlinear systems, where only the output time series is available. [1][2][4][5]. The work reported here is an attempt to implement few of the well known algorithms in the context of modeling of nonlinear systems, and to make a performance comparison to establish the relative merits and demerits.

Keywords: Multilayer neural networks, Radial Basis Functions, Clustering algorithm, Back Propagation training, Extended Kalmanfiltering, Mean Square Error, Nonlinear Modeling, Cramer RaoLower Bound.

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Authors: Adeolu O. Dairo

Abstract:

Getting the consumer marketing strategy right is a crucial and complex task for firms with a large customer base such as mobile operators in a competitive mobile market. While empirical studies have made efforts to identify key constructs, no geospatial model has been developed to comprehensively assess the viability and interdependency of ground realities regarding the customer, competition, channel and the network quality of mobile operators. With this research, a geo-analytic framework is proposed for strategy formulation and allocation for mobile operators. Firstly, a fuzzy analytic network using a self-organizing feature map clustering technique based on inputs from managers and literature, which depicts the interrelationships amongst ground realities is developed. The model is tested with a mobile operator in the Nigeria mobile market. As a result, a customer-centric geospatial and visualization solution is developed. This provides a consolidated and integrated insight that serves as a transparent, logical and practical guide for strategic, tactical and operational decision making.

Keywords: Geospatial, geo-analytics, self-organizing map, customer-centric.

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Authors: Parvinder S. Sandhu, Sunil Khullar, Satpreet Singh, Simranjit K. Bains, Manpreet Kaur, Gurvinder Singh

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Fault-proneness of a software module is the probability that the module contains faults. To predict faultproneness of modules different techniques have been proposed which includes statistical methods, machine learning techniques, neural network techniques and clustering techniques. The aim of proposed study is to explore whether metrics available in the early lifecycle (i.e. requirement metrics), metrics available in the late lifecycle (i.e. code metrics) and metrics available in the early lifecycle (i.e. requirement metrics) combined with metrics available in the late lifecycle (i.e. code metrics) can be used to identify fault prone modules using Genetic Algorithm technique. This approach has been tested with real time defect C Programming language datasets of NASA software projects. The results show that the fusion of requirement and code metric is the best prediction model for detecting the faults as compared with commonly used code based model.

Keywords: Genetic Algorithm, Fault Proneness, Software Faultand Software Quality.

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Authors: Jaime R. S. Fonseca

Abstract:

Nevertheless the widespread application of finite mixture models in segmentation, finite mixture model selection is still an important issue. In fact, the selection of an adequate number of segments is a key issue in deriving latent segments structures and it is desirable that the selection criteria used for this end are effective. In order to select among several information criteria, which may support the selection of the correct number of segments we conduct a simulation study. In particular, this study is intended to determine which information criteria are more appropriate for mixture model selection when considering data sets with only categorical segmentation base variables. The generation of mixtures of multinomial data supports the proposed analysis. As a result, we establish a relationship between the level of measurement of segmentation variables and some (eleven) information criteria-s performance. The criterion AIC3 shows better performance (it indicates the correct number of the simulated segments- structure more often) when referring to mixtures of multinomial segmentation base variables.

Keywords: Quantitative Methods, Multivariate Data Analysis, Clustering, Finite Mixture Models, Information Theoretical Criteria, Simulation experiments.

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Authors: Abder-Rahman Ali, Antoine Vacavant, Manuel Grand-Brochier, Adélaïde Albouy-Kissi, Jean-Yves Boire

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In this paper, we present a new segmentation approach for liver lesions in regions of interest within MRI (Magnetic Resonance Imaging). This approach, based on a two-cluster Fuzzy CMeans methodology, considers the parameter variable compactness to handle uncertainty. Fine boundaries are detected by a local recursive merging of ambiguous pixels with a sequential forward floating selection with Zernike moments. The method has been tested on both synthetic and real images. When applied on synthetic images, the proposed approach provides good performance, segmentations obtained are accurate, their shape is consistent with the ground truth, and the extracted information is reliable. The results obtained on MR images confirm such observations. Our approach allows, even for difficult cases of MR images, to extract a segmentation with good performance in terms of accuracy and shape, which implies that the geometry of the tumor is preserved for further clinical activities (such as automatic extraction of pharmaco-kinetics properties, lesion characterization, etc.).

Keywords: Defuzzification, floating search, fuzzy clustering, Zernike moments.

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Authors: J. Hossen, A. Rahman, K. Samsudin, F. Rokhani, S. Sayeed, R. Hasan

Abstract:

The Neuro-Fuzzy hybridization scheme has become of research interest in pattern classification over the past decade. The present paper proposes a novel Modified Adaptive Fuzzy Inference Engine (MAFIE) for pattern classification. A modified Apriori algorithm technique is utilized to reduce a minimal set of decision rules based on input output data sets. A TSK type fuzzy inference system is constructed by the automatic generation of membership functions and rules by the fuzzy c-means clustering and Apriori algorithm technique, respectively. The generated adaptive fuzzy inference engine is adjusted by the least-squares fit and a conjugate gradient descent algorithm towards better performance with a minimal set of rules. The proposed MAFIE is able to reduce the number of rules which increases exponentially when more input variables are involved. The performance of the proposed MAFIE is compared with other existing applications of pattern classification schemes using Fisher-s Iris and Wisconsin breast cancer data sets and shown to be very competitive.

Keywords: Apriori algorithm, Fuzzy C-means, MAFIE, TSK

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