Search results for: Spatial Clustering

Authors: Yan Xu, Toshihiro Nishimura

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Segmentation in ultrasound images is challenging due to the interference from speckle noise and fuzziness of boundaries. In this paper, a segmentation scheme using fuzzy c-means (FCM) clustering incorporating both intensity and texture information of images is proposed to extract breast lesions in ultrasound images. Firstly, the nonlinear structure tensor, which can facilitate to refine the edges detected by intensity, is used to extract speckle texture. And then, a spatial FCM clustering is applied on the image feature space for segmentation. In the experiments with simulated and clinical ultrasound images, the spatial FCM clustering with both intensity and texture information gets more accurate results than the conventional FCM or spatial FCM without texture information.

Keywords: fuzzy c-means, spatial information, structure tensor, ultrasound image segmentation

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Authors: M.Sujaritha, S. Annadurai

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An adaptive spatial Gaussian mixture model is proposed for clustering based color image segmentation. A new clustering objective function which incorporates the spatial information is introduced in the Bayesian framework. The weighting parameter for controlling the importance of spatial information is made adaptive to the image content to augment the smoothness towards piecewisehomogeneous region and diminish the edge-blurring effect and hence the name adaptive spatial finite mixture model. The proposed approach is compared with the spatially variant finite mixture model for pixel labeling. The experimental results with synthetic and Berkeley dataset demonstrate that the proposed method is effective in improving the segmentation and it can be employed in different practical image content understanding applications.

Keywords: Adaptive; Spatial, Mixture model, Segmentation, Color.

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Authors: A.M. Fahim, A. M. Salem, F. A. Torkey, M. A. Ramadan

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Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. In this paper, a density based clustering algorithm (DCBRD) is presented, relying on a knowledge acquired from the data by dividing the data space into overlapped regions. The proposed algorithm discovers arbitrary shaped clusters, requires no input parameters and uses the same definitions of DBSCAN algorithm. We performed an experimental evaluation of the effectiveness and efficiency of it, and compared this results with that of DBSCAN. The results of our experiments demonstrate that the proposed algorithm is significantly efficient in discovering clusters of arbitrary shape and size.

Keywords: Clustering Algorithms, Arbitrary Shape of clusters, cluster Analysis.

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Authors: Sauravjyoti Sarmah, Rosy Das, Dhruba Kr. Bhattacharyya

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Clustering algorithms help to understand the hidden information present in datasets. A dataset may contain intrinsic and nested clusters, the detection of which is of utmost importance. This paper presents a Distributed Grid-based Density Clustering algorithm capable of identifying arbitrary shaped embedded clusters as well as multi-density clusters over large spatial datasets. For handling massive datasets, we implemented our method using a 'sharednothing' architecture where multiple computers are interconnected over a network. Experimental results are reported to establish the superiority of the technique in terms of scale-up, speedup as well as cluster quality.

Keywords: Clustering, Density-based, Grid-based, Adaptive Grid.

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Authors: Seo Young Kim, Tai Myong Choi

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The main goal of microarray experiments is to quantify the expression of every object on a slide as precisely as possible, with a further goal of clustering the objects. Recently, many studies have discussed clustering issues involving similar patterns of gene expression. This paper presents an application of fuzzy-type methods for clustering DNA microarray data that can be applied to typical comparisons. Clustering and analyses were performed on microarray and simulated data. The results show that fuzzy-possibility c-means clustering substantially improves the findings obtained by others.

Keywords: Clustering, microarray data, Fuzzy-type clustering, Validation

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Authors: Lubna Eljabu, Mohammad Etemad, Stan Matwin

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The automatic extraction of shipping routes is advantageous for intelligent traffic management systems to identify events and support decision-making in maritime surveillance. At present, there is a high demand for the extraction of maritime traffic networks that resemble the real traffic of vessels accurately, which is valuable for further analytical processing tasks for vessels trajectories (e.g., naval routing and voyage planning, anomaly detection, destination prediction, time of arrival estimation). With the help of big data and processing huge amounts of vessels’ trajectory data, it is possible to learn these shipping routes from the navigation history of past behaviour of other, similar ships that were travelling in a given area. In this paper, we propose a spatial clustering model of vessels’ trajectories (SPTCLUST) to extract spatial representations of sailing routes from historical Automatic Identification System (AIS) data. The whole model consists of three main parts: data preprocessing, path finding, and route extraction, which consists of clustering and representative trajectory extraction. The proposed clustering method provides techniques to overcome the problems of: (i) optimal input parameters selection; (ii) the high complexity of processing a huge volume of multidimensional data; (iii) and the spatial representation of complete representative trajectory detection in the context of trajectory clustering algorithms. The experimental evaluation showed the effectiveness of the proposed model by using a real-world AIS dataset from the Port of Halifax. The results contribute to further understanding of shipping route patterns. This could aid surveillance authorities in stable and sustainable vessel traffic management.

Keywords: Vessel trajectory clustering, trajectory mining, Spatial Clustering, marine intelligent navigation, maritime traffic network extraction, sdailing routes extraction.

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Authors: In-Chul Jung, Young S. Kwon

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Knowing about the customer behavior in a grocery has been a long-standing issue in the retailing industry. The advent of RFID has made it easier to collect moving data for an individual shopper's behavior. Most of the previous studies used the traditional statistical clustering technique to find the major characteristics of customer behavior, especially shopping path. However, in using the clustering technique, due to various spatial constraints in the store, standard clustering methods are not feasible because moving data such as the shopping path should be adjusted in advance of the analysis, which is time-consuming and causes data distortion. To alleviate this problem, we propose a new approach to spatial pattern clustering based on the longest common subsequence. Experimental results using real data obtained from a grocery confirm the good performance of the proposed method in finding the hot spot, dead spot and major path patterns of customer movements.

Keywords: customer path, shopping behavior, exploratoryanalysis, LCS, RFID

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Authors: Bainian Li, Kongsheng Zhang, Jian Xu

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In this paper we study the fuzzy c-mean clustering algorithm combined with principal components method. Demonstratively analysis indicate that the new clustering method is well rather than some clustering algorithms. We also consider the validity of clustering method.

Keywords: FCM algorithm, Principal Components Analysis, Clustervalidity

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Authors: Pornpimol Bungkomkhun, Surapong Auwatanamongkol

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This paper presents a supervised clustering algorithm, namely Grid-Based Supervised Clustering (GBSC), which is able to identify clusters of any shapes and sizes without presuming any canonical form for data distribution. The GBSC needs no prespecified number of clusters, is insensitive to the order of the input data objects, and is capable of handling outliers. Built on the combination of grid-based clustering and density-based clustering, under the assistance of the downward closure property of density used in bottom-up subspace clustering, the GBSC can notably reduce its search space to avoid the memory confinement situation during its execution. On two-dimension synthetic datasets, the GBSC can identify clusters with different shapes and sizes correctly. The GBSC also outperforms other five supervised clustering algorithms when the experiments are performed on some UCI datasets.

Keywords: supervised clustering, grid-based clustering, subspace clustering

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Authors: Neveen I. Ghali, Nahed El-Dessouki, Mervat A. N., Lamiaa Bakrawi

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In this paper we use exponential particle swarm optimization (EPSO) to cluster data. Then we compare between (EPSO) clustering algorithm which depends on exponential variation for the inertia weight and particle swarm optimization (PSO) clustering algorithm which depends on linear inertia weight. This comparison is evaluated on five data sets. The experimental results show that EPSO clustering algorithm increases the possibility to find the optimal positions as it decrease the number of failure. Also show that (EPSO) clustering algorithm has a smaller quantization error than (PSO) clustering algorithm, i.e. (EPSO) clustering algorithm more accurate than (PSO) clustering algorithm.

Keywords: Particle swarm optimization, data clustering, exponential PSO.

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Authors: Sara El Manar El Bouanani, Ismail Kassou

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Our objective in this paper is to propose an approach capable of clustering web messages. The clustering is carried out by assigning, with a certain probability, texts written by the same web user to the same cluster based on Stylometric features and using fuzzy clustering algorithms. Focus in the present work is on comparing the most popular algorithms in fuzzy clustering theory namely, Fuzzy C-means, Possibilistic C-means and Fuzzy Possibilistic C-Means.

Keywords: Authorship detection, fuzzy clustering, profiling, stylometric features.

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Authors: S. Sarumathi, N. Shanthi, M. Sharmila

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Clustering in data mining is an unsupervised learning technique of aggregating the data objects into meaningful groups such that the intra cluster similarity of objects are maximized and inter cluster similarity of objects are minimized. Over the past decades several clustering tools were emerged in which clustering algorithms are inbuilt and are easier to use and extract the expected results. Data mining mainly deals with the huge databases that inflicts on cluster analysis and additional rigorous computational constraints. These challenges pave the way for the emergence of powerful expansive data mining clustering softwares. In this survey, a variety of clustering tools used in data mining are elucidated along with the pros and cons of each software.

Keywords: Cluster Analysis, Clustering Algorithms, Clustering Techniques, Association, Visualization.

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Authors: Z. Abdullah, A. R. Hamdan

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Clustering is a process of grouping objects and data into groups of clusters to ensure that data objects from the same cluster are identical to each other. Clustering algorithms in one of the area in data mining and it can be classified into partition, hierarchical, density based and grid based. Therefore, in this paper we do survey and review four major hierarchical clustering algorithms called CURE, ROCK, CHAMELEON and BIRCH. The obtained state of the art of these algorithms will help in eliminating the current problems as well as deriving more robust and scalable algorithms for clustering.

Keywords: Clustering, method, algorithm, hierarchical, survey.

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Authors: Reza Ghaemi , Md. Nasir Sulaiman , Hamidah Ibrahim , Norwati Mustapha

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The clustering ensembles combine multiple partitions generated by different clustering algorithms into a single clustering solution. Clustering ensembles have emerged as a prominent method for improving robustness, stability and accuracy of unsupervised classification solutions. So far, many contributions have been done to find consensus clustering. One of the major problems in clustering ensembles is the consensus function. In this paper, firstly, we introduce clustering ensembles, representation of multiple partitions, its challenges and present taxonomy of combination algorithms. Secondly, we describe consensus functions in clustering ensembles including Hypergraph partitioning, Voting approach, Mutual information, Co-association based functions and Finite mixture model, and next explain their advantages, disadvantages and computational complexity. Finally, we compare the characteristics of clustering ensembles algorithms such as computational complexity, robustness, simplicity and accuracy on different datasets in previous techniques.

Keywords: Clustering Ensembles, Combinational Algorithm, Consensus Function, Unsupervised Classification.

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Authors: Sumiko Anno, Keiji Imaoka, Takeo Tadono, Tamotsu Igarashi, Subramaniam Sivaganesh, Selvam Kannathasan, Vaithehi Kumaran, Sinnathamby Noble Surendran

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Dengue outbreaks are affected by biological, ecological, socio-economic and demographic factors that vary over time and space. These factors have been examined separately and still require systematic clarification. The present study aimed to investigate the spatial-temporal clustering relationships between these factors and dengue outbreaks in the northern region of Sri Lanka. Remote sensing (RS) data gathered from a plurality of satellites were used to develop an index comprising rainfall, humidity and temperature data. RS data gathered by ALOS/AVNIR-2 were used to detect urbanization, and a digital land cover map was used to extract land cover information. Other data on relevant factors and dengue outbreaks were collected through institutions and extant databases. The analyzed RS data and databases were integrated into geographic information systems, enabling temporal analysis, spatial statistical analysis and space-time clustering analysis. Our present results showed that increases in the number of the combination of ecological factor and socio-economic and demographic factors with above the average or the presence contribute to significantly high rates of space-time dengue clusters.

Keywords: ALOS/AVNIR-2, Dengue, Space-time clustering analysis, Sri Lanka.

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Authors: Hmway Hmway Tar, Thi Thi Soe Nyaunt

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Documents clustering become an essential technology with the popularity of the Internet. That also means that fast and high-quality document clustering technique play core topics. Text clustering or shortly clustering is about discovering semantically related groups in an unstructured collection of documents. Clustering has been very popular for a long time because it provides unique ways of digesting and generalizing large amounts of information. One of the issues of clustering is to extract proper feature (concept) of a problem domain. The existing clustering technology mainly focuses on term weight calculation. To achieve more accurate document clustering, more informative features including concept weight are important. Feature Selection is important for clustering process because some of the irrelevant or redundant feature may misguide the clustering results. To counteract this issue, the proposed system presents the concept weight for text clustering system developed based on a k-means algorithm in accordance with the principles of ontology so that the important of words of a cluster can be identified by the weight values. To a certain extent, it has resolved the semantic problem in specific areas.

Keywords: Clustering, Concept Weight, Document clustering, Feature Selection, Ontology

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Authors: Achmad Nizar Hidayanto, Elisabeth Martha Koeanan

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Image clustering is a process of grouping images based on their similarity. The image clustering usually uses the color component, texture, edge, shape, or mixture of two components, etc. This research aims to explore image clustering using color composition. In order to complete this image clustering, three main components should be considered, which are color space, image representation (feature extraction), and clustering method itself. We aim to explore which composition of these factors will produce the best clustering results by combining various techniques from the three components. The color spaces use RGB, HSV, and L*a*b* method. The image representations use Histogram and Gaussian Mixture Model (GMM), whereas the clustering methods use KMeans and Agglomerative Hierarchical Clustering algorithm. The results of the experiment show that GMM representation is better combined with RGB and L*a*b* color space, whereas Histogram is better combined with HSV. The experiments also show that K-Means is better than Agglomerative Hierarchical for images clustering.

Keywords: Image clustering, feature extraction, RGB, HSV, L*a*b*, Gaussian Mixture Model (GMM), histogram, Agglomerative Hierarchical Clustering (AHC), K-Means, Expectation-Maximization (EM).

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Authors: Jung-Eun Park, Kyung-Whan Oh

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Intelligent systems are required in order to quickly and accurately analyze enormous quantities of data in the Internet environment. In intelligent systems, information extracting processes can be divided into supervised learning and unsupervised learning. This paper investigates intelligent clustering by unsupervised learning. Intelligent clustering is the clustering system which determines the clustering model for data analysis and evaluates results by itself. This system can make a clustering model more rapidly, objectively and accurately than an analyzer. The methodology for the automatic clustering intelligent system is a multi-agent system that comprises a clustering agent and a cluster performance evaluation agent. An agent exchanges information about clusters with another agent and the system determines the optimal cluster number through this information. Experiments using data sets in the UCI Machine Repository are performed in order to prove the validity of the system.

Keywords: Intelligent Clustering, Multi-Agent System, PCA, SOM, VC(Variance Criterion)

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Authors: Miin-Shen Yang, Yee-Shan Pan

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Although there have been many researches in cluster analysis to consider on feature weights, little effort is made on sample weights. Recently, Yu et al. (2011) considered a probability distribution over a data set to represent its sample weights and then proposed sample-weighted clustering algorithms. In this paper, we give a sample-weighted version of generalized fuzzy clustering regularization (GFCR), called the sample-weighted GFCR (SW-GFCR). Some experiments are considered. These experimental results and comparisons demonstrate that the proposed SW-GFCR is more effective than the most clustering algorithms.

Keywords: Clustering; fuzzy c-means, fuzzy clustering, sample weights, regularization.

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Authors: Mohd. Noor Md. Sap, A. Majid Awan

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Intelligent systems based on machine learning techniques, such as classification, clustering, are gaining wide spread popularity in real world applications. This paper presents work on developing a software system for predicting crop yield, for example oil-palm yield, from climate and plantation data. At the core of our system is a method for unsupervised partitioning of data for finding spatio-temporal patterns in climate data using kernel methods which offer strength to deal with complex data. This work gets inspiration from the notion that a non-linear data transformation into some high dimensional feature space increases the possibility of linear separability of the patterns in the transformed space. Therefore, it simplifies exploration of the associated structure in the data. Kernel methods implicitly perform a non-linear mapping of the input data into a high dimensional feature space by replacing the inner products with an appropriate positive definite function. In this paper we present a robust weighted kernel k-means algorithm incorporating spatial constraints for clustering the data. The proposed algorithm can effectively handle noise, outliers and auto-correlation in the spatial data, for effective and efficient data analysis by exploring patterns and structures in the data, and thus can be used for predicting oil-palm yield by analyzing various factors affecting the yield.

Keywords: Pattern analysis, clustering, kernel methods, spatial data, crop yield

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Authors: T. Niknam, M. Nayeripour, B.Bahmani Firouzi

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Clustering techniques have received attention in many areas including engineering, medicine, biology and data mining. The purpose of clustering is to group together data points, which are close to one another. The K-means algorithm is one of the most widely used techniques for clustering. However, K-means has two shortcomings: dependency on the initial state and convergence to local optima and global solutions of large problems cannot found with reasonable amount of computation effort. In order to overcome local optima problem lots of studies done in clustering. This paper is presented an efficient hybrid evolutionary optimization algorithm based on combining Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), called PSO-ACO, for optimally clustering N object into K clusters. The new PSO-ACO algorithm is tested on several data sets, and its performance is compared with those of ACO, PSO and K-means clustering. The simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for handing data clustering.

Keywords: Ant Colony Optimization (ACO), Data clustering, Hybrid evolutionary optimization algorithm, K-means clustering, Particle Swarm Optimization (PSO).

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Authors: Guadalupe J. Torres, Ram B. Basnet, Andrew H. Sung, Srinivas Mukkamala, Bernardete M. Ribeiro

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This paper introduces a measure of similarity between two clusterings of the same dataset produced by two different algorithms, or even the same algorithm (K-means, for instance, with different initializations usually produce different results in clustering the same dataset). We then apply the measure to calculate the similarity between pairs of clusterings, with special interest directed at comparing the similarity between various machine clusterings and human clustering of datasets. The similarity measure thus can be used to identify the best (in terms of most similar to human) clustering algorithm for a specific problem at hand. Experimental results pertaining to the text categorization problem of a Portuguese corpus (wherein a translation-into-English approach is used) are presented, as well as results on the well-known benchmark IRIS dataset. The significance and other potential applications of the proposed measure are discussed.

Keywords: Clustering Algorithms, Clustering Applications, Similarity Measures, Text Clustering

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Authors: Uttam Vijay, Nitin Gupta

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Due to heavy energy constraints in WSNs clustering is an efficient way to manage the energy in sensors. There are many methods already proposed in the area of clustering and research is still going on to make clustering more energy efficient. In our paper we are proposing a minimum spanning tree based clustering using divide and conquer approach. The MST based clustering was first proposed in 1970’s for large databases. Here we are taking divide and conquer approach and implementing it for wireless sensor networks with the constraints attached to the sensor networks. This Divide and conquer approach is implemented in a way that we don’t have to construct the whole MST before clustering but we just find the edge which will be the part of the MST to a corresponding graph and divide the graph in clusters there itself if that edge from the graph can be removed judging on certain constraints and hence saving lot of computation.

Keywords: Algorithm, Clustering, Edge-Weighted Graph, Weighted-LEACH.

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Authors: Ágnes Vathy-Fogarassy, Balázs Feil, János Abonyi

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Most of fuzzy clustering algorithms have some discrepancies, e.g. they are not able to detect clusters with convex shapes, the number of the clusters should be a priori known, they suffer from numerical problems, like sensitiveness to the initialization, etc. This paper studies the synergistic combination of the hierarchical and graph theoretic minimal spanning tree based clustering algorithm with the partitional Gath-Geva fuzzy clustering algorithm. The aim of this hybridization is to increase the robustness and consistency of the clustering results and to decrease the number of the heuristically defined parameters of these algorithms to decrease the influence of the user on the clustering results. For the analysis of the resulted fuzzy clusters a new fuzzy similarity measure based tool has been presented. The calculated similarities of the clusters can be used for the hierarchical clustering of the resulted fuzzy clusters, which information is useful for cluster merging and for the visualization of the clustering results. As the examples used for the illustration of the operation of the new algorithm will show, the proposed algorithm can detect clusters from data with arbitrary shape and does not suffer from the numerical problems of the classical Gath-Geva fuzzy clustering algorithm.

Keywords: Clustering, fuzzy clustering, minimal spanning tree, cluster validity, fuzzy similarity.

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Authors: S. Barbosa, M. Pinto, J. A. Almeida, E. Carvalho, C. Diamantino

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The aim of this work was to test a methodology able to generate spatial-temporal maps that can synthesize simultaneously the trends of distinct hydrochemical indicators in an old radium-uranium tailings dam deposit. Multidimensionality reduction derived from principal component analysis and subsequent data aggregation derived from clustering analysis allow to identify distinct hydrochemical behavioral profiles and generate synthetic evolutionary hydrochemical maps.

Keywords: Contamination plume migration, K-means of PCA scores, groundwater and mine water monitoring, spatial-temporal hydrochemical trends.

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Authors: Fahad Shahbaz Khan, Rao Muhammad Anwer, Olof Torgersson

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The vast amount of information hidden in huge databases has created tremendous interests in the field of data mining. This paper examines the possibility of using data clustering techniques in oral medicine to identify functional relationships between different attributes and classification of similar patient examinations. Commonly used data clustering algorithms have been reviewed and as a result several interesting results have been gathered.

Keywords: Oral Medicine, Cluto, Data Clustering, Data Mining.

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Authors: Qin Ding, Jim Gasvoda

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Clustering is the process of subdividing an input data set into a desired number of subgroups so that members of the same subgroup are similar and members of different subgroups have diverse properties. Many heuristic algorithms have been applied to the clustering problem, which is known to be NP Hard. Genetic algorithms have been used in a wide variety of fields to perform clustering, however, the technique normally has a long running time in terms of input set size. This paper proposes an efficient genetic algorithm for clustering on very large data sets, especially on image data sets. The genetic algorithm uses the most time efficient techniques along with preprocessing of the input data set. We test our algorithm on both artificial and real image data sets, both of which are of large size. The experimental results show that our algorithm outperforms the k-means algorithm in terms of running time as well as the quality of the clustering.

Keywords: Clustering, data mining, genetic algorithm, image data.

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Authors: Binu Thomas, Raju G., Sonam Wangmo

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In Data mining, Fuzzy clustering algorithms have demonstrated advantage over crisp clustering algorithms in dealing with the challenges posed by large collections of vague and uncertain natural data. This paper reviews concept of fuzzy logic and fuzzy clustering. The classical fuzzy c-means algorithm is presented and its limitations are highlighted. Based on the study of the fuzzy c-means algorithm and its extensions, we propose a modification to the cmeans algorithm to overcome the limitations of it in calculating the new cluster centers and in finding the membership values with natural data. The efficiency of the new modified method is demonstrated on real data collected for Bhutan-s Gross National Happiness (GNH) program.

Keywords: Adaptive fuzzy clustering, clustering, fuzzy logic, fuzzy clustering, c-means.

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Authors: Liping Jing, Michael K. Ng, Xinhua Yang, Joshua Zhexue Huang

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This paper presents a text clustering system developed based on a k-means type subspace clustering algorithm to cluster large, high dimensional and sparse text data. In this algorithm, a new step is added in the k-means clustering process to automatically calculate the weights of keywords in each cluster so that the important words of a cluster can be identified by the weight values. For understanding and interpretation of clustering results, a few keywords that can best represent the semantic topic are extracted from each cluster. Two methods are used to extract the representative words. The candidate words are first selected according to their weights calculated by our new algorithm. Then, the candidates are fed to the WordNet to identify the set of noun words and consolidate the synonymy and hyponymy words. Experimental results have shown that the clustering algorithm is superior to the other subspace clustering algorithms, such as PROCLUS and HARP and kmeans type algorithm, e.g., Bisecting-KMeans. Furthermore, the word extraction method is effective in selection of the words to represent the topics of the clusters.

Keywords: Subspace Clustering, Text Mining, Feature Weighting, Cluster Interpretation, Ontology

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Authors: Sunita Jahirabadkar, Parag Kulkarni

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Many real-world data sets consist of a very high dimensional feature space. Most clustering techniques use the distance or similarity between objects as a measure to build clusters. But in high dimensional spaces, distances between points become relatively uniform. In such cases, density based approaches may give better results. Subspace Clustering algorithms automatically identify lower dimensional subspaces of the higher dimensional feature space in which clusters exist. In this paper, we propose a new clustering algorithm, ISC – Intelligent Subspace Clustering, which tries to overcome three major limitations of the existing state-of-art techniques. ISC determines the input parameter such as є – distance at various levels of Subspace Clustering which helps in finding meaningful clusters. The uniform parameters approach is not suitable for different kind of databases. ISC implements dynamic and adaptive determination of Meaningful clustering parameters based on hierarchical filtering approach. Third and most important feature of ISC is the ability of incremental learning and dynamic inclusion and exclusions of subspaces which lead to better cluster formation.

Keywords: Density based clustering, high dimensional data, subspace clustering, dynamic parameter setting.

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