The k-means algorithm with cosine similarity, also known as the spherical k-means algorithm, is a popular method for clustering document collections. However, spherical k-means ca...
Detecting outliers which are grossly different from or inconsistent with the remaining dataset is a major challenge in real-world KDD applications. Existing outlier detection met...
: In real-life data, in general, many attribute values are missing. Therefore, rule induction requires preprocessing, where missing attribute values are replaced by appropriate val...
Jerzy W. Grzymala-Busse, Witold J. Grzymala-Busse,...
Knowledge discovery in databases has become an increasingly important research topic with the advent of wide area network computing. One of the crucial problems we study in this p...
Gene expression information from microarray experiments is a primary form of data for biological analysis and can offer insights into disease processes and cellular behaviour. Suc...