By Junjie Wu
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.
Read Online or Download Advances in K-means Clustering: a Data Mining Thinking PDF
Best data mining books
This ebook constitutes the refereed complaints of the Brazilian Symposium on Bioinformatics, BSB 2005, held in Sao Leopoldo, Brazil in July 2005. The 15 revised complete papers and 10 revised prolonged abstracts provided including three invited papers have been rigorously reviewed and chosen from fifty five submissions.
This ebook constitutes the refereed lawsuits of the sixth foreign convention on Geographic details technological know-how, GIScience 2010, held in Zurich, Switzerland, in September 2010. The 22 revised complete papers provided have been rigorously reviewed and chosen from 87 submissions. whereas conventional examine themes equivalent to spatio-temporal representations, spatial kinfolk, interoperability, geographic databases, cartographic generalization, geographic visualization, navigation, spatial cognition, are alive and good in GIScience, study on tips to deal with colossal and quickly turning out to be databases of dynamic space-time phenomena at fine-grained solution for instance, generated via sensor networks, has in actual fact emerged as a brand new and renowned examine frontier within the box.
This quantity includes the papers offered on the 18th foreign Conf- ence on Algorithmic studying thought (ALT 2007), which was once held in Sendai (Japan) in the course of October 1–4, 2007. the most target of the convention used to be to supply an interdisciplinary discussion board for high quality talks with a robust theore- cal history and scienti?
"Cut guaranty expenses by way of lowering fraud with obvious tactics and balanced keep watch over guaranty Fraud administration presents a transparent, useful framework for decreasing fraudulent guaranty claims and different extra charges in guaranty and repair operations. filled with actionable guidance and certain info, this booklet lays out a method of effective guaranty administration that could lessen expenses with out frightening the client dating.
- Hybrid Artificial Intelligence Systems: 4th International Conference, HAIS 2009, Salamanca, Spain, June 10-12, 2009, Proceedings
- Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry: Third International Conference, MDA
- Transactions on Large-Scale Data- and Knowledge-Centered Systems XXVIII: Special Issue on Database- and Expert-Systems Applications
- Scala: Guide for Data Science Professionals
Additional info for Advances in K-means Clustering: a Data Mining Thinking
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 , 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 . Breunig et al.  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 , or objects that are farthest from their corresponding cluster centroids . Another research direction is to handle outliers during the clustering process.
Advances in K-means Clustering: a Data Mining Thinking by Junjie Wu