Read e-book online Data Clustering: Theory, Algorithms, and Applications PDF

By Guojun Gan

ISBN-10: 0898716233

ISBN-13: 9780898716238

Cluster research is an unmanaged method that divides a suite of gadgets into homogeneous teams. This e-book begins with simple info on cluster research, together with the category of information and the corresponding similarity measures, through the presentation of over 50 clustering algorithms in teams in response to a few particular baseline methodologies reminiscent of hierarchical, center-based, and search-based equipment. for this reason, readers and clients can simply establish a suitable set of rules for his or her functions and evaluate novel rules with latest effects. The e-book additionally presents examples of clustering purposes to demonstrate the benefits and shortcomings of alternative clustering architectures and algorithms. software parts comprise trend popularity, synthetic intelligence, details expertise, photograph processing, biology, psychology, and advertising. Readers additionally easy methods to practice cluster research with the C/C++ and MATLAB® programming languages. viewers the next teams will locate this booklet a helpful device and reference: utilized statisticians; engineers and scientists utilizing info research; researchers in trend acceptance, man made intelligence, desktop studying, and information mining; and utilized mathematicians. teachers may also use it as a textbook for an introductory direction in cluster research or as resource fabric for a graduate-level advent to facts mining. Contents Preface; bankruptcy 1: facts Clustering; bankruptcy 2: info varieties; bankruptcy three: Scale Conversion; bankruptcy four: information Standardizatin and Transformation; bankruptcy five: facts Visualization; bankruptcy 6: Similarity and Dissimilarity Measures; bankruptcy 7: Hierarchical Clustering recommendations; bankruptcy eight: Fuzzy Clustering Algorithms; bankruptcy nine: heart established Clustering Algorithms; bankruptcy 10: seek established Clustering Algorithms; bankruptcy eleven: Graph dependent Clustering Algorithms; Chatper 12: Grid dependent Clustering Algorithms; bankruptcy thirteen: Density dependent Clustering Algorithms; bankruptcy 14: version established Clustering Algorithms; bankruptcy 15: Subspace Clustering; bankruptcy sixteen: Miscellaneous Algorithms; bankruptcy 17: overview of Clustering Algorithms; bankruptcy 18: Clustering Gene Expression info; bankruptcy 19: information Clustering in MATLAB; bankruptcy 20: Clustering in C/C++; Appendix A: a few Clustering Algorithms; Appendix B: Thekd-tree info constitution; Appendix C: MATLAB Codes; Appendix D: C++ Codes; topic Index; writer Index

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40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. Chapter 1. Data Clustering Computer Computers & Mathematics with Applications Computational Statistics and Data Analysis Discrete and Computational Geometry The Computer Journal Data Mining and Knowledge Discovery Engineering Applications of Artificial Intelligence European Journal of Operational Research Future Generation Computer Systems Fuzzy Sets and Systems Genome Biology Knowledge and Information Systems The Indian Journal of Statistics IEEE Transactions on Evolutionary Computation IEEE Transactions on Information Theory IEEE Transactions on Image Processing IEEE Transactions on Knowledge and Data Engineering IEEE Transactions on Neural Networks IEEE Transactions on Pattern Analysis and Machine Intelligence IEEE Transactions on Systems, Man, and Cybernetics IEEE Transactions on Systems, Man, and Cybernetics, Part B IEEE Transactions on Systems, Man, and Cybernetics, Part C Information Sciences Journal of the ACM Journal of the American Society for Information Science Journal of the American Statistical Association Journal of the Association for Computing Machinery Journal of Behavioral Health Services and Research Journal of Chemical Information and Computer Sciences Journal of Classification Journal of Complexity Journal of Computational and Applied Mathematics Journal of Computational and Graphical Statistics Journal of Ecology Journal of Global Optimization Journal of Marketing Research Journal of the Operational Research Society Journal of the Royal Statistical Society.

Then a symbol table Ts of the data set is defined as Ts = (s1 , s2 , . . 1) where sj (1 ≤ j ≤ d) is a vector defined as sj = (Aj 1 , Aj 2 , . . , Aj nj )T . Since there are possibly multiple states (or values) for a variable, a symbol table of a data set is usually not unique. 3 are its symbol tables. The frequency table is computed according to a symbol table and it has exactly the same dimension as the symbol table. Let C be a cluster. Then the frequency table Tf (C) of cluster C is defined as Tf (C) = (f1 (C), f2 (C), .

7. Here Ward’s method is used to cluster a one-dimensional data set. (i) Iterative improvement of a partition. The method of iterative improvement of a partition tries to find the partition of a one-dimensional data set that minimizes the sum of squares. 2. (j) The linear discriminant function. For one-dimensional data, the multiple group discriminant analysis can be described as follows. Suppose there are g normal populations present in the grand ensemble in proportions p1 , p2 , . . , pg . If an observation from population j is classified wrongly as being from population i, then a loss L(i : j ) is incurred.

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Data Clustering: Theory, Algorithms, and Applications by Guojun Gan


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