Search results for: Mining Tools

Authors: T. Suresh Kumar, M. Arthanari, N. Shanthi

Abstract:

Nowadays, the Web has become one of the most pervasive platforms for information change and retrieval. It collects the suitable and perfectly fitting information from websites that one requires. Data mining is the form of extracting data’s available in the internet. Web mining is one of the elements of data mining Technique, which relates to various research communities such as information recovery, folder managing system and simulated intellects. In this Paper we have discussed the concepts of Web mining. We contain generally focused on one of the categories of Web mining, specifically the Web Content Mining and its various farm duties. The mining tools are imperative to scanning the many images, text, and HTML documents and then, the result is used by the various search engines. We conclude by presenting a comparative table of these tools based on some pertinent criteria.

Keywords: Data Mining, Web Mining, Web Content Mining, Mining Tools, Information retrieval.

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

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There have been a lot of efforts and researches undertaken in developing efficient tools for performing several tasks in data mining. Due to the massive amount of information embedded in huge data warehouses maintained in several domains, the extraction of meaningful pattern is no longer feasible. This issue turns to be more obligatory for developing several tools in data mining. Furthermore the major aspire of data mining software is to build a resourceful predictive or descriptive model for handling large amount of information more efficiently and user friendly. Data mining mainly contracts with excessive collection of data that inflicts huge rigorous computational constraints. These out coming challenges lead to the emergence of powerful data mining technologies. In this survey a diverse collection of data mining tools are exemplified and also contrasted with the salient features and performance behavior of each tool.

Keywords: Business Analytics, Data Mining, Data Analysis, Machine Learning, Text Mining, Predictive Analytics, Visualization.

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Authors: S. Sarumathi, N. Shanthi

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Due to the fast and flawless technological innovation there is a tremendous amount of data dumping all over the world in every domain such as Pattern Recognition, Machine Learning, Spatial Data Mining, Image Analysis, Fraudulent Analysis, World Wide Web etc., This issue turns to be more essential for developing several tools for data mining functionalities. The major aim of this paper is to analyze various tools which are used to build a resourceful analytical or descriptive model for handling large amount of information more efficiently and user friendly. In this survey the diverse tools are illustrated with their extensive technical paradigm, outstanding graphical interface and inbuilt multipath algorithms in which it is very useful for handling significant amount of data more indeed.

Keywords: Classification, Clustering, Data Mining, Machine learning, Visualization.

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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: Hoda A. Abdel Hafez

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Mining big data represents a big challenge nowadays. Many types of research are concerned with mining massive amounts of data and big data streams. Mining big data faces a lot of challenges including scalability, speed, heterogeneity, accuracy, provenance and privacy. In telecommunication industry, mining big data is like a mining for gold; it represents a big opportunity and maximizing the revenue streams in this industry. This paper discusses the characteristics of big data (volume, variety, velocity and veracity), data mining techniques and tools for handling very large data sets, mining big data in telecommunication and the benefits and opportunities gained from them.

Keywords: Mining Big Data, Big Data, Machine learning, Data Streams, Telecommunication.

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Authors: Mária Stachová, Lukáš Sobíšek

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Data mining incorporates a group of statistical methods used to analyze a set of information, or a data set. It operates with models and algorithms, which are powerful tools with the great potential. They can help people to understand the patterns in certain chunk of information so it is obvious that the data mining tools have a wide area of applications. For example in the theoretical chemistry data mining tools can be used to predict moleculeproperties or improve computer-assisted drug design. Classification analysis is one of the major data mining methodologies. The aim of thecontribution is to create a classification model, which would be able to deal with a huge data set with high accuracy. For this purpose logistic regression, Bayesian logistic regression and random forest models were built using R software. TheBayesian logistic regression in Latent GOLD software was created as well. These classification methods belong to supervised learning methods. It was necessary to reduce data matrix dimension before construct models and thus the factor analysis (FA) was used. Those models were applied to predict the biological activity of molecules, potential new drug candidates.

Keywords: data mining, classification, drug design, QSAR

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Authors: Shaun Cox, Michael Oakes, Stefan Wermter, Maurice Hawthorne

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We report on the results of a pilot study in which a data-mining tool was developed for mining audiology records. The records were heterogeneous in that they contained numeric, category and textual data. The tools developed are designed to observe associations between any field in the records and any other field. The techniques employed were the statistical chi-squared test, and the use of self-organizing maps, an unsupervised neural learning approach.

Keywords: Audiology, data mining, chi-squared, self-organizing maps

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Authors: Nittaya Kerdprasop, Kittisak Kerdprasop

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Data mining (DM) is the process of finding and extracting frequent patterns that can describe the data, or predict unknown or future values. These goals are achieved by using various learning algorithms. Each algorithm may produce a mining result completely different from the others. Some algorithms may find millions of patterns. It is thus the difficult job for data analysts to select appropriate models and interpret the discovered knowledge. In this paper, we describe a framework of an intelligent and complete data mining system called SUT-Miner. Our system is comprised of a full complement of major DM algorithms, pre-DM and post-DM functionalities. It is the post-DM packages that ease the DM deployment for business intelligence applications.

Keywords: Business intelligence, data mining, functionalprogramming, intelligent system.

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Authors: M. Awawdeh, A. Fedi

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The application of data mining to environmental monitoring has become crucial for a number of tasks related to emergency management. Over recent years, many tools have been developed for decision support system (DSS) for emergency management. In this article a graphical user interface (GUI) for environmental monitoring system is presented. This interface allows accomplishing (i) data collection and observation and (ii) extraction for data mining. This tool may be the basis for future development along the line of the open source software paradigm.

Keywords: Data Mining, Environmental data, Mathematical Models, Matlab Graphical User Interface.

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Authors: M. Castellano, G. Mastronardi, A. Aprile, G. Tarricone

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Text Mining is an important step of Knowledge Discovery process. It is used to extract hidden information from notstructured o semi-structured data. This aspect is fundamental because much of the Web information is semi-structured due to the nested structure of HTML code, much of the Web information is linked, much of the Web information is redundant. Web Text Mining helps whole knowledge mining process to mining, extraction and integration of useful data, information and knowledge from Web page contents. In this paper, we present a Web Text Mining process able to discover knowledge in a distributed and heterogeneous multiorganization environment. The Web Text Mining process is based on flexible architecture and is implemented by four steps able to examine web content and to extract useful hidden information through mining techniques. Our Web Text Mining prototype starts from the recovery of Web job offers in which, through a Text Mining process, useful information for fast classification of the same are drawn out, these information are, essentially, job offer place and skills.

Keywords: Web text mining, flexible architecture, knowledgediscovery.

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Authors: Thiago M. R. Dias, Gray F. Moita, Paulo E. M. Almeida

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Although the World Wide Web is considered the largest source of information there exists nowadays, due to its inherent dynamic characteristics, the task of finding useful and qualified information can become a very frustrating experience. This study presents a research on the information mining systems in the Web; and proposes an implementation of these systems by means of components that can be built using the technology of Web services. This implies that they can encompass features offered by a services oriented architecture (SOA) and specific components may be used by other tools, independent of platforms or programming languages. Hence, the main objective of this work is to provide an architecture to Web mining systems, divided into stages, where each step is a component that will incorporate the characteristics of SOA. The separation of these steps was designed based upon the existing literature. Interesting results were obtained and are shown here.

Keywords: Web Mining, Service Oriented Architecture, WebServices.

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Authors: Hector D. Cortes, Jesus A. del Rio, Esther O. Garcia, Miguel Robles

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Recently the use of data mining to scientific bibliographic data bases has been implemented to analyze the pathways of the knowledge or the core scientific relevances of a laureated novel or a country. This specific case of data mining has been named citation mining, and it is the integration of citation bibliometrics and text mining. In this paper we present an improved WEB implementation of statistical physics algorithms to perform the text mining component of citation mining. In particular we use an entropic like distance between the compression of text as an indicator of the similarity between them. Finally, we have included the recently proposed index h to characterize the scientific production. We have used this web implementation to identify users, applications and impact of the Mexican scientific institutions located in the State of Morelos.

Keywords: Citation Mining, Text Mining, Science Impact

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Authors: Jyoti Dua

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One image is worth more than thousand words. Images if analyzed can reveal useful information. Low level image processing deals with the extraction of specific feature from a single image. Now the question arises: What technique should be used to extract patterns of very large and detailed image database? The answer of the question is: “Image Mining”. Image Mining deals with the extraction of image data relationship, implicit knowledge, and another pattern from the collection of images or image database. It is nothing but the extension of Data Mining. In the following paper, not only we are going to scrutinize the current techniques of image mining but also present a new technique for mining images using Genetic Algorithm.

Keywords: Image Mining, Data Mining, Genetic Algorithm.

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Authors: G. Poonkuzhali, K.Thiagarajan, K.Sarukesi, G.V.Uma

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The emergence of the Internet has brewed the revolution of information storage and retrieval. As most of the data in the web is unstructured, and contains a mix of text, video, audio etc, there is a need to mine information to cater to the specific needs of the users without loss of important hidden information. Thus developing user friendly and automated tools for providing relevant information quickly becomes a major challenge in web mining research. Most of the existing web mining algorithms have concentrated on finding frequent patterns while neglecting the less frequent ones that are likely to contain outlying data such as noise, irrelevant and redundant data. This paper mainly focuses on Signed approach and full word matching on the organized domain dictionary for mining web content outliers. This Signed approach gives the relevant web documents as well as outlying web documents. As the dictionary is organized based on the number of characters in a word, searching and retrieval of documents takes less time and less space.

Keywords: Outliers, Relevant document, , Signed Approach, Web content mining, Web documents..

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Authors: Jing Lu, Malcolm Keech, Weiru Chen

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Web usage mining is an interesting application of data mining which provides insight into customer behaviour on the Internet. An important technique to discover user access and navigation trails is based on sequential patterns mining. One of the key challenges for web access patterns mining is tackling the problem of mining richly structured patterns. This paper proposes a novel model called Web Access Patterns Graph (WAP-Graph) to represent all of the access patterns from web mining graphically. WAP-Graph also motivates the search for new structural relation patterns, i.e. Concurrent Access Patterns (CAP), to identify and predict more complex web page requests. Corresponding CAP mining and modelling methods are proposed and shown to be effective in the search for and representation of concurrency between access patterns on the web. From experiments conducted on large-scale synthetic sequence data as well as real web access data, it is demonstrated that CAP mining provides a powerful method for structural knowledge discovery, which can be visualised through the CAP-Graph model.

Keywords: concurrent access patterns (CAP), CAP mining and modelling, CAP-Graph, web access patterns (WAP), WAP-Graph, Web usage mining.

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Authors: Patrick Wessa, Bart Baesens

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This paper discusses the use of explorative data mining tools that allow the educator to explore new relationships between reported learning experiences and actual activities, even if there are multiple dimensions with a large number of measured items. The underlying technology is based on the so-called Compendium Platform for Reproducible Computing (http://www.freestatistics.org) which was built on top the computational R Framework (http://www.wessa.net).

Keywords: Reproducible computing, data mining, explorative data analysis, compendium technology, computer assisted education

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Authors: Yu Zhang, Pedro Desouza

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Since big data has become substantially more accessible and manageable due to the development of powerful tools for dealing with unstructured data, people are eager to mine information from social media resources that could not be handled in the past. Sentiment analysis, as a novel branch of text mining, has in the last decade become increasingly important in marketing analysis, customer risk prediction and other fields. Scientists and researchers have undertaken significant work in creating and improving their sentiment models. In this paper, we present a concept of selecting appropriate classifiers based on the features and qualities of data sources by comparing the performances of five classifiers with three popular social media data sources: Twitter, Amazon Customer Reviews, and Movie Reviews. We introduced a couple of innovative models that outperform traditional sentiment classifiers for these data sources, and provide insights on how to further improve the predictive power of sentiment analysis. The modeling and testing work was done in R and Greenplum in-database analytic tools.

Keywords: Sentiment Analysis, Social Media, Twitter, Amazon, Data Mining, Machine Learning, Text Mining.

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Authors: S. Kannimuthu, K. Premalatha

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This paper investigates a new data mining capability that entails mining of High Utility Itemsets (HUI) in a distributed environment. Existing research in data mining deals with only presence or absence of an items and do not consider the semantic measures like weight or cost of the items. Thus, HUI mining algorithm has evolved. HUI mining is the one kind of utility mining concept, aims to identify itemsets whose utility satisfies a given threshold. Although, the approach of mining HUIs in a distributed environment and mining of the same from XML data have not explored yet. In this work, a novel approach is proposed to mine HUIs from the XML based data in a distributed environment. This work utilizes Service Oriented Computing (SOC) paradigm which provides Knowledge as a Service (KaaS). The interesting patterns are provided via the web services with the help of knowledge server to answer the queries of the consumers. The performance of the approach is evaluated on various databases using execution time and memory consumption.

Keywords: Data mining, Knowledge as a Service, service oriented computing, utility mining.

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Authors: G. Poonkuzhali, K.Thiagarajan, K.Sarukesi

Abstract:

With the enormous growth on the web, users get easily lost in the rich hyper structure. Thus developing user friendly and automated tools for providing relevant information without any redundant links to the users to cater to their needs is the primary task for the website owners. Most of the existing web mining algorithms have concentrated on finding frequent patterns while neglecting the less frequent one that are likely to contain the outlying data such as noise, irrelevant and redundant data. This paper proposes new algorithm for mining the web content by detecting the redundant links from the web documents using set theoretical(classical mathematics) such as subset, union, intersection etc,. Then the redundant links is removed from the original web content to get the required information by the user..

Keywords: Web documents, Web content mining, redundantlink, outliers, set theory.

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Authors: Suresh Shirgave, Prakash Kulkarni, José Borges

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The continuous growth in the size of the World Wide Web has resulted in intricate Web sites, demanding enhanced user skills and more sophisticated tools to help the Web user to find the desired information. In order to make Web more user friendly, it is necessary to provide personalized services and recommendations to the Web user. For discovering interesting and frequent navigation patterns from Web server logs many Web usage mining techniques have been applied. The recommendation accuracy of usage based techniques can be improved by integrating Web site content and site structure in the personalization process.

Herein, we propose semantically enriched Web Usage Mining method for Personalization (SWUMP), an extension to solely usage based technique. This approach is a combination of the fields of Web Usage Mining and Semantic Web. In the proposed method, we envisage enriching the undirected graph derived from usage data with rich semantic information extracted from the Web pages and the Web site structure. The experimental results show that the SWUMP generates accurate recommendations and is able to achieve 10-20% better accuracy than the solely usage based model. The SWUMP addresses the new item problem inherent to solely usage based techniques.

Keywords: Prediction, Recommendation, Semantic Web Usage Mining, Web Usage Mining.

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Authors: Charles D. Otine, Samuel B. Kucel, Lena Trojer

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Selecting the data modeling technique for an information system is determined by the objective of the resultant data model. Dimensional modeling is the preferred modeling technique for data destined for data warehouses and data mining, presenting data models that ease analysis and queries which are in contrast with entity relationship modeling. The establishment of data warehouses as components of information system landscapes in many organizations has subsequently led to the development of dimensional modeling. This has been significantly more developed and reported for the commercial database management systems as compared to the open sources thereby making it less affordable for those in resource constrained settings. This paper presents dimensional modeling of HIV patient information using open source modeling tools. It aims to take advantage of the fact that the most affected regions by the HIV virus are also heavily resource constrained (sub-Saharan Africa) whereas having large quantities of HIV data. Two HIV data source systems were studied to identify appropriate dimensions and facts these were then modeled using two open source dimensional modeling tools. Use of open source would reduce the software costs for dimensional modeling and in turn make data warehousing and data mining more feasible even for those in resource constrained settings but with data available.

Keywords: About Database, Data Mining, Data warehouse, Dimensional Modeling, Open Source.

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Authors: Carlo Bellettini, Alessandro Marchetto, Andrea Trentini

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Web applications have become very complex and crucial, especially when combined with areas such as CRM (Customer Relationship Management) and BPR (Business Process Reengineering), the scientific community has focused attention to Web applications design, development, analysis, and testing, by studying and proposing methodologies and tools. This paper proposes an approach to automatic multi-dimensional concern mining for Web Applications, based on concepts analysis, impact analysis, and token-based concern identification. This approach lets the user to analyse and traverse Web software relevant to a particular concern (concept, goal, purpose, etc.) via multi-dimensional separation of concerns, to document, understand and test Web applications. This technique was developed in the context of WAAT (Web Applications Analysis and Testing) project. A semi-automatic tool to support this technique is currently under development.

Keywords: Aspect Mining, Concepts Analysis, Concerns Mining, Multi-Dimensional Separation of Concerns, Impact Analysis.

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Authors: K. Jahani, J. Razavi

Abstract:

Nowadays, to decrease the number of downtimes in the industries such as metal mining, petroleum and chemical industries, predictive maintenance is crucial. In order to have efficient predictive maintenance, knowing the performance of critical equipment of production line such as pumps and hydro-cyclones under variable operating parameters, selecting best indicators of this equipment health situations, best locations for instrumentation, and also measuring of these indicators are very important. In this paper, computer aided engineering (CAE) tools are implemented to study some important elements of copper process line, namely slurry pumps and cyclone to predict the performance of these components under different working conditions. These modeling and simulations can be used in predicting, for example, the damage tolerance of the main shaft of the slurry pump or wear rate and location of cyclone wall or pump case and impeller. Also, the simulations can suggest best-measuring parameters, measuring intervals, and their locations.

Keywords: Computer aided engineering, predictive maintenance, fault detection, mining process line, slurry pump, hydrocyclone.

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Authors: Li Gao, Elizabeth Chang, Song Han

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With the extensive inclusion of document, especially text, in the business systems, data mining does not cover the full scope of Business Intelligence. Data mining cannot deliver its impact on extracting useful details from the large collection of unstructured and semi-structured written materials based on natural languages. The most pressing issue is to draw the potential business intelligence from text. In order to gain competitive advantages for the business, it is necessary to develop the new powerful tool, text mining, to expand the scope of business intelligence. In this paper, we will work out the strong points of text mining in extracting business intelligence from huge amount of textual information sources within business systems. We will apply text mining to each stage of Business Intelligence systems to prove that text mining is the powerful tool to expand the scope of BI. After reviewing basic definitions and some related technologies, we will discuss the relationship and the benefits of these to text mining. Some examples and applications of text mining will also be given. The motivation behind is to develop new approach to effective and efficient textual information analysis. Thus we can expand the scope of Business Intelligence using the powerful tool, text mining.

Keywords: Business intelligence, document warehouse, text mining.

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Authors: A. Haery, N. Salmasi, M. Modarres Yazdi, H. Iranmanesh

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In this paper the application of rule mining in order to review the effective factors on supplier selection is reviewed in the following three sections 1) criteria selecting and information gathering 2) performing association rule mining 3) validation and constituting rule base. Afterwards a few of applications of rule base is explained. Then, a numerical example is presented and analyzed by Clementine software. Some of extracted rules as well as the results are presented at the end.

Keywords: Association rule mining, data mining, supplierselection criteria.

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Authors: Muslihah Wook, Zawiyah M. Yusof, Mohd Zakree Ahmad Nazri

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Data mining has been integrated into application systems to enhance the quality of the decision-making process. This study aims to focus on the integration of data mining technology and Knowledge Management System (KMS), due to the ability of data mining technology to create useful knowledge from large volumes of data. Meanwhile, KMS vitally support the creation and use of knowledge. The integration of data mining technology and KMS are popularly used in business for enhancing and sustaining organizational performance. However, there is a lack of studies that applied data mining technology and KMS in the education sector; particularly students- academic performance since this could reflect the IHL performance. Realizing its importance, this study seeks to integrate data mining technology and KMS to promote an effective management of knowledge within IHLs. Several concepts from literature are adapted, for proposing the new integrative data mining technology and KMS framework to an IHL.

Keywords: Data mining, Institutions of Higher Learning, Knowledge Management System, Students' academic performance.

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Authors: Kamal Ali Albashiri, Khaled Ahmed Kadouh

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A generic and extendible Multi-Agent Data Mining (MADM) framework, MADMF (the Multi-Agent Data Mining Framework) is described. The central feature of the framework is that it avoids the use of agreed meta-language formats by supporting a framework of wrappers. The advantage offered is that the framework is easily extendible, so that further data agents and mining agents can simply be added to the framework. A demonstration MADMF framework is currently available. The paper includes details of the MADMF architecture and the wrapper principle incorporated into it. A full description and evaluation of the framework-s operation is provided by considering two MADM scenarios.

Keywords: Multi-Agent Data Mining (MADM), Frequent Itemsets, Meta ARM, Association Rule Mining, Classifier generator.

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Authors: H. Baazaoui Zghal, S. Faiz, H. Ben Ghezala

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Data mining is an extraordinarily demanding field referring to extraction of implicit knowledge and relationships, which are not explicitly stored in databases. A wide variety of methods of data mining have been introduced (classification, characterization, generalization...). Each one of these methods includes more than algorithm. A system of data mining implies different user categories,, which mean that the user-s behavior must be a component of the system. The problem at this level is to know which algorithm of which method to employ for an exploratory end, which one for a decisional end, and how can they collaborate and communicate. Agent paradigm presents a new way of conception and realizing of data mining system. The purpose is to combine different algorithms of data mining to prepare elements for decision-makers, benefiting from the possibilities offered by the multi-agent systems. In this paper the agent framework for data mining is introduced, and its overall architecture and functionality are presented. The validation is made on spatial data. Principal results will be presented.

Keywords: Databases, data mining, multi-agent, spatial datamart.

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Authors: M. Moistsrapishvili, I. Ugrekhelidze, T. Baramashvili, D. Malaghuradze

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At the end of the 20th century it was actual the development of transport corridors and the improvement of their technical parameters. With this purpose, many countries and Georgia among them manufacture to construct new highways, railways and also reconstruction-modernization of the existing transport infrastructure. It is necessary to explore the artificial structures (bridges and tunnels) on the existing tracks as they are very old. Conference report includes the peculiarities of reconstruction of tunnels, because we think that this theme is important for the modernization of the existing road infrastructure. We must remark that the methods of determining mining pressure of tunnel reconstructions are worked out according to the jobs of new tunnels but it is necessary to foresee additional mining pressure which will be formed during their reconstruction. In this report there are given the methods of figuring the additional mining pressure while reconstruction of tunnels, there was worked out the computer program, it is determined that during reconstruction of tunnels the additional mining pressure is 1/3rd of main mining pressure.

Keywords: Mining pressure, Reconstruction of tunnels.

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Authors: Anahita Farhang Ghahfarokhi, Alessandro Berti, Wil M.P. van der Aalst

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Process mining provides ways to analyze business processes. Common process mining techniques consider the process as a whole. However, in real-life business processes, different behaviors exist that make the overall process too complex to interpret. Process comparison is a branch of process mining that isolates different behaviors of the process from each other by using process cubes. Process cubes organize event data using different dimensions. Each cell contains a set of events that can be used as an input to apply process mining techniques. Existing work on process cubes assume single case notions. However, in real processes, several case notions (e.g., order, item, package, etc.) are intertwined. Object-centric process mining is a new branch of process mining addressing multiple case notions in a process. To make a bridge between object-centric process mining and process comparison, we propose a process cube framework, which supports process cube operations such as slice and dice on object-centric event logs. To facilitate the comparison, the framework is integrated with several object-centric process discovery approaches.

Keywords: Process mining, multidimensional process mining, multi-perspective business processes, OLAP, process cubes, process discovery.

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