Liu has written a comprehensive text on Web mining, which consists of two parts. The first
part covers the data mining and machine learning foundations, where all the essential …

Subspace clustering refers to the task of identifying clusters of similar objects or data records
(vectors) where the similarity is defined with respect to a subset of the attributes (ie, a …

The rapid growth of the Web in the past two decades has made it the largest publicly
accessible data source in the world. Web mining aims to discover useful information or …

Subspace clustering finds sets of objects that are homogeneous in subspaces of high-
dimensional datasets, and has been successfully applied in many domains. In recent years …

Cluster analysis has long played an important role in a broad variety of areas, such as
psychology, biology, computer sciences. It has established as a precious tool for marketing …

In clustering analysis, data attributes may have different contributions to the detection of
various clusters. To solve this problem, the subspace clustering technique has been …

To gain insight into today's large data resources, data mining provides automatic
aggregation techniques. Clustering aims at grouping data such that objects within groups …

Due to data sparseness and attribute redundancy in high-dimensional data, clusters of
objects often exist in subspaces rather than in the entire space. To effectively address this …

Attribute independence has been taken as a major assumption in the limited research that
has been conducted on similarity analysis for categorical data, especially unsupervised …

Traditional clustering algorithms consider all of the dimensions of an input data set equally.
However, in the high dimensional data, a common property is that data points are highly …