Advances in K-means Clustering: a Data Mining Thinking by Junjie Wu PDF

By Junjie Wu

ISBN-10: 3642298079

ISBN-13: 9783642298073

Nearly we all know K-means set of rules within the fields of knowledge mining and company intelligence. however the ever-emerging facts with super complex features convey new demanding situations to this "old" set of rules. This publication addresses those demanding situations and makes novel contributions in constructing theoretical frameworks for K-means distances and K-means established consensus clustering, deciding upon the "dangerous" uniform influence and zero-value quandary of K-means, adapting correct measures for cluster validity, and integrating K-means with SVMs for infrequent classification research. This ebook not just enriches the clustering and optimization theories, but in addition offers solid information for the sensible use of K-means, specially for vital initiatives similar to community intrusion detection and credits fraud prediction. The thesis on which this publication relies has received the "2010 nationwide very good Doctoral Dissertation Award", the top honor for no more than a hundred PhD theses in step with yr in China.

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IEEE Trans. Syst. Man Cybern. Part B 29(3), 433–439 (1999) 18. : Fast and effective text mining using linear-time document clustering. In: Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 16–22 (1999) 19. : Clustering massive data sets. G. ) Handbook of Massive Data Sets, pp. 501–543. Kluwer Academic Publishers, Norwell (2002) 20. : An algorithm for suffix stripping. Program 14(3), 130–137 (1980) 21. : Intrusion detection with unlabeled data using clustering.

Further, φ is strictly convex if and only if ∀ x = y, φ(x) − φ( y) − (x − y)T ∇φ( y) > 0. 2 is well-known as the first-order convexity condition. The proof can be found in pages 69–70 in [6], which we omit here. Now, based on the above two lemmas, we can derive a necessary condition for f being a distance function that fits GD-FCM directly. We have the following theorem. 4 Let S ⊆ R be a nonempty open convex set. Assume f : S × S → R+ is a continuously differentiable function satisfying: (1) f (x, x) = 0, ∀ x ∈ S ; (2) f y (x, y) is continuously differentiable on x.

For instance, a simple method of detecting outliers is based on the distance measure [16]. Breunig et al. [4] proposed a density based method using the Local Outlier Factor (LOF) for the purpose of identifying outliers in data with varying densities. There are also some other clustering based methods to detect outliers as small and remote clusters [21], or objects that are farthest from their corresponding cluster centroids [18]. Another research direction is to handle outliers during the clustering process.

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