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

By Junjie Wu

Nearly we all know K-means set of rules within the fields of information mining and enterprise intelligence. however the ever-emerging information with tremendous advanced features carry new demanding situations to this "old" set of rules. This e-book addresses those demanding situations and makes novel contributions in developing theoretical frameworks for K-means distances and K-means dependent consensus clustering, deciding on the "dangerous" uniform impression and zero-value predicament of K-means, adapting correct measures for cluster validity, and integrating K-means with SVMs for infrequent category research. This booklet not just enriches the clustering and optimization theories, but in addition offers stable information for the sensible use of K-means, specifically for very important initiatives comparable to community intrusion detection and credits fraud prediction. The thesis on which this publication is predicated has gained the "2010 nationwide very good Doctoral Dissertation Award", the top honor for no more than a hundred PhD theses in line with yr in China.

Show description

Read Online or Download Advances in K-means Clustering: a Data Mining Thinking PDF

Best data mining books

The Top Ten Algorithms in Data Mining

Picking out probably the most influential algorithms which are common within the info mining group, the head Ten Algorithms in info Mining offers an outline of every set of rules, discusses its effect, and experiences present and destiny learn. completely evaluated by means of self reliant reviewers, each one bankruptcy specializes in a selected set of rules and is written via both the unique authors of the set of rules or world-class researchers who've broadly studied the respective set of rules.

Data Mining: Concepts, Models and Techniques

The information discovery procedure is as previous as Homo sapiens. till a while in the past this approach was once exclusively in response to the ‘natural own' laptop supplied through mom Nature. thankfully, in fresh a long time the matter has all started to be solved in response to the improvement of the knowledge mining expertise, aided by way of the large computational energy of the 'artificial' pcs.

Computational Science and Its Applications – ICCSA 2014: 14th International Conference, Guimarães, Portugal, June 30 – July 3, 2014, Proceedings, Part VI

The six-volume set LNCS 8579-8584 constitutes the refereed court cases of the 14th overseas convention on Computational technology and Its functions, ICCSA 2014, held in Guimarães, Portugal, in June/July 2014. The 347 revised papers awarded in 30 workshops and a distinct tune have been conscientiously reviewed and chosen from 1167.

Scala: Guide for Data Science Professionals

Scala could be a precious device to have available in the course of your facts technology trip for every little thing from information cleansing to state of the art computer learningAbout This BookBuild info technological know-how and information engineering recommendations with easeAn in-depth examine every one level of the information research strategy — from examining and amassing information to allotted analyticsExplore a wide number of facts processing, computer studying, and genetic algorithms via diagrams, mathematical formulations, and resource codeWho This booklet Is ForThis studying direction is ideal if you are ok with Scala programming and now are looking to input the sphere of knowledge technological know-how.

Extra resources for Advances in K-means Clustering: a Data Mining Thinking

Example text

42 3 Generalizing Distance Functions for Fuzzy c-Means Clustering Let the solution set = {(U ∗ , V ∗ ) ∈ M f c ×Rcd |Jm (U ∗ , V ∗ ) ≤ Jm (U, V ∗ ) ∀ U ∈ M f c , and Jm (U ∗ , V ∗ ) < Jm (U ∗ , V ) ∀ V = V ∗ }. 3 ([16]) Given the fuzzy c-means problem defined by Eqs. 2), let (U (0) , G(U (0) )) be the starting point of iteration with Tm , where U (0) ∈ M f c . ∞ either terminates at a point in , or Then the iteration sequence ((U (l) , V (l) ))l=0 there is a subsequence converging to a point in .

Extensive experiments on a number of real-world data sets clearly illustrate the uniform effect of K-means and the biased effect of the entropy measure, via the help of the Coefficient of Variation statistic. Most importantly, we unveil the danger induced by the combined use of K-means and the entropy measure. That is, many true clusters will become unidentifiable when applying K-means for highly imbalanced data, but this situation is often disguised by the low values of the entropy measure. References 1.

12), we can know that Eq. 13) is true. Discussion. By Eq. 12), we know that the minimization of the K-means objective function Fk is equivalent to the maximization of the distance function FD(k) , where (k) both Dk and n are constants for a given data set. For FD in Eq. e. e. n 1 = n 2 = · · · = n k = n/k. Note that we have isolated the effect of two components: m i − m j 2 and n i n j here to simplify the discussion. For real-world data sets, however, these two components are interactive. 3 The Relationship between K-means Clustering and the Entropy Measure In this section, we study the relationship between K-means clustering and a widely used clustering validation measure: Entropy (E).

Download PDF sample

Rated 4.37 of 5 – based on 18 votes