Data analysis plays an indispensable role for understanding various phenomena. Cluster
analysis, primitive exploration with little or no prior knowledge, consists of research …

This paper presents the top 10 data mining algorithms identified by the IEEE International
Conference on Data Mining (ICDM) in December 2006: C4. 5, k-Means, SVM, Apriori, EM …

In k-means clustering, we are given a set of n data points in d-dimensional space R/sup
d/and an integer k and the problem is to determine a set of k points in Rd, called centers, so …

Cluster analysis is a primary method for database mining. It is either used as a stand-alone
tool to get insight into the distribution of a data set, eg to focus further analysis and data …

This paper introduces the problem of combining multiple partitionings of a set of objects into
a single consolidated clustering without accessing the features or algorithms that …

METHOD The algorithm requires as input a matrix of M points in N dimensions and a matrix
of K initial cluster centres in N dimensions. The number of points in cluster L is denoted by …

Knowledge discovery in databases presents many interesting challenges within the context
of providing computer tools for exploring large data archives. Electronic data repositories are …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

A unified, coherent treatment of current classifier ensemble methods, from fundamentals of
pattern recognition to ensemble feature selection, now in its second edition The art and …

The authors present a fuzzy validity criterion based on a validity function which identifies
compact and separate fuzzy c-partitions without assumptions as to the number of …