The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

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Data Mining: Concepts and Techniques

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, we present the new clustering algorithm DBSCAN relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN requires only one input parameter and supports the user in determining an appropriate value for it. We performed an experimental evaluation of the effectiveness and efficiency of DBSCAN using synthetic data and real data of the SEQUOIA 2000 benchmark. The results of our experiments demonstrate that (1) DBSCAN is significantly more effective in discovering clusters of arbitrary shape than the well-known algorithm CLAR-ANS, and that (2) DBSCAN outperforms CLARANS by a factor of more than 100 in terms of efficiency.

A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise

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, we present the new clustering algorithm DBSCAN relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN requires only one input parameter and supports the user in determining an appropriate value for it. We performed an experimental evaluation of the effectiveness and efficiency of DBSCAN using synthetic data and real data of the SEQUOIA 2000 benchmark. The results of our experiments demonstrate that (1) DBSCAN is significantly more effective in discovering clusters of arbitrary shape than the well-known algorithm CLARANS, and that (2) DBSCAN outperforms CLARANS by a factor of more than 100 in terms of efficiency.

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A density-based algorithm for discovering clusters in large spatial Databases with Noise

Correlation networks are increasingly being used in bioinformatics applications For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets These methods have been successfully applied in various biological contexts, eg cancer, mouse genetics, yeast genetics, and analysis of brain imaging data While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software Along with the R package we also present R software tutorials While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings The WGCNA package provides R functions for weighted correlation network analysis, eg co-expression network analysis of gene expression data The R package along with its source code and additional material are freely available at http://wwwgeneticsuclaedu/labs/horvath/CoexpressionNetwork/Rpackages/WGCNA
 

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WGCNA: an R package for weighted correlation network analysis.

Silhouettes: a graphical aid to the interpretation and validation of cluster analysis

1. Introduction. 2. Partitioning Around Medoids (Program PAM). 3. Clustering large Applications (Program CLARA). 4. Fuzzy Analysis. 5. Agglomerative Nesting (Program AGNES). 6. Divisive Analysis (Program DIANA). 7. Monothetic Analysis (Program MONA). Appendix 1. Implementation and Structure of the Programs. Appendix 2. Running the Programs. Appendix 3. Adapting the Programs to Your Needs. Appendix 4. The Program CLUSPLOT. References. Author Index. Subject Index.

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Finding Groups in Data: An Introduction to Cluster Analysis

An electrical signal transmission system, applicable to the transmission of signals from trackside hot box detector equipment for railroad locomotives and rolling stock, wherein a basic pulse train is transmitted whereof the pulses are of a selected first amplitude and represent a train axle count, and a spike pulse of greater selected amplitude is transmitted, occurring immediately after the axle count pulse to which it relates, whenever an overheated axle box is detected. To enable the signal receiving equipment to determine on which side of a train the overheated box is located, the spike pulses are of two different amplitudes corresponding, respectively, to opposite sides of the train.

Finding Groups in Data

background Type 1 diabetes mellitus is a T-cell–mediated autoimmune disease that leads to a major loss of insulin-secreting beta cells. The further decline of beta-cell function after clinical onset might be prevented by treatment with CD3 monoclonal antibodies, as suggested by the results of a phase 1 study. To provide proof of this therapeutic principle at the metabolic level, we initiated a phase 2 placebo-controlled trial with a humanized antibody, an aglycosylated human IgG1 antibody directed against CD3 (ChAglyCD3). methods In a multicenter study, 80 patients with new-onset type 1 diabetes were randomly assigned to receive placebo or ChAglyCD3 for six consecutive days. Patients were followed for 18 months, during which their daily insulin needs and residual beta-cell function were assessed according to glucose-clamp–induced C-peptide release before and after the administration of glucagon. results At 6, 12, and 18 months, residual beta-cell function was better maintained with ChAglyCD3 than with placebo. The insulin dose increased in the placebo group but not in the ChAglyCD3 group. This effect of ChAglyCD3 was most pronounced among patients with initial residual beta-cell function at or above the 50th percentile of the 80 patients. In this subgroup, the mean insulin dose at 18 months was 0.22 IU per kilogram of body weight per day with ChAglyCD3, as compared with 0.61 IU per kilogram with placebo (P<0.001). In this subgroup, 12 of 16 patients who received ChAglyCD3 (75 percent) received minimal doses of insulin (≤0.25 IU per kilogram per day) as compared with none of the 21 patients who received placebo. Administration of ChAglyCD3 was associated with a moderate “flu-like” syndrome and transient symptoms of Epstein–Barr viral mononucleosis. conclusions Short-term treatment with CD3 antibody preserves residual beta-cell function for at least 18 months in patients with recent-onset type 1 diabetes.

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Insulin needs after CD3-antibody therapy in new-onset type 1 diabetes.

Finding Groups in Data: An Introduction to Cluster Analysis.

A practical, user-oriented introduction to clustering methods, including both hierarchical and non-hierarchical methods. Shows how clustering can be used to interpret large quantities of analytical data and discusses the relation of clustering to other pattern recognition techniques. Uses a two-level approach to provide both a qualitative understanding of the philosophy, advantages, and disadvantages of clustering and a quantitative understanding for readers who want a strong mathematical background. Includes a worked example and a list of computer packages.

Leonard Kaufman

Papers

Finding Groups in Data: An Introduction to Cluster Analysis

Finding Groups in Data

Insulin needs after CD3-antibody therapy in new-onset type 1 diabetes.

Finding Groups in Data: An Introduction to Cluster Analysis.

The Interpretation of Analytical Chemical Data by the Use of Cluster Analysis