In network traffic classification, it is important to understand the correlation between network
traffic and its causal application, protocol, or service group, for example, in facilitating lawful …

We survey agglomerative hierarchical clustering algorithms and discuss efficient
implementations that are available in R and other software environments. We look at …

The partitional clustering concept started with K-means algorithm which was published in
1957. Since then many classical partitional clustering algorithms have been reported based …

The monograph Data Clustering: Theory, Algorithms, and Applications was published in
2007. Starting with the common ground and knowledge for data clustering, the monograph …

Introduction to Data Mining Page 1 CHAPTER 14 ■ ■ ■ 369 Introduction to Data Mining In
this chapter, we’ll explore the incredibly powerful tools included with SQL Server Analysis …

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

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 …

Business intelligence is a broad category of applications and technologies for gathering,
providing access to, and analyzing data for the purpose of helping enterprise users make …

The research community has begun looking for IP traffic classification techniques that do not
rely onwell known'TCP or UDP port numbers, or interpreting the contents of packet …

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 …