Data analysis is used as a common method in modern science research, which is across
communication science, computer science and biology science. Clustering, as the basic …

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

Clustering is the division of data into groups of similar objects. In clustering, some details are
disregarded in exchange for data simplification. Clustering can be viewed as a data …

This chapter reviews visualization techniques that can not only be utilized to map the
evergrowing domain structure of scientific disciplines but that also support information …

This is the first book to take a truly comprehensive look at clustering. It begins with an
introduction to cluster analysis and goes on to explore: proximity measures; hierarchical …

As a prolific research area in data mining, subspace clustering and related problems
induced a vast quantity of proposed solutions. However, many publications compare a new …

In the last two decades, machine learning research and practice has focused on batch
learning, usually with small datasets. Nowadays there are applications in which the data are …

Outlier detection is an integral part of data mining and has attracted much attention recently
[M. Breunig et al.,(2000)],[W. ** et al.,(2001)],[E. Knorr et al.,(2000)]. We propose a new …

Outlier detection is a primary step in many data-mining applications. We present several
methods for outlier detection, while distinguishing between univariate vs. multivariate …

The fast-growing Internet results in massive amounts of text data. Due to the large volume of
the unstructured format of text data, extracting relevant information and its analysis becomes …