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 …

The growing volume of information databases presents opportunities for advanced data
analysis techniques from machine learning (ML) research. Practical applications of ML are …

Mixed data comprises both numeric and categorical features, and mixed datasets occur
frequently in many domains, such as health, finance, and marketing. Clustering is often …

Use of traditional k-mean type algorithm is limited to numeric data. This paper presents a
clustering algorithm based on k-mean paradigm that works well for data with mixed numeric …

Presents a similarity-based agglomerative clustering (SBAC) algorithm that works well for
data with mixed numeric and nominal features. A similarity measure proposed by DW …

Clustering is often used for discovering structure in data. Clustering systems differ in the
objective function used to evaluate clustering quality and the control strategy used to search …

Clustering is one of the most important data mining techniques that partitions data according
to some similarity criterion. The problems of clustering categorical data have attracted much …

This paper describes a new approach to find the right clustering of a dataset. We have
developed a genetic algorithm to perform this task. A simple encoding scheme that yields to …

Clustering is a fundamental technique in data mining and pattern recognition, which has
been successfully applied in several contexts. However, most of the clustering algorithms …

Clustering is the search for those partitions that re ect the structure of an object set.
Traditional clustering algorithms search only a small sub-set of all possible clusterings (the …