Finding useful patterns in large datasets has attracted considerable interest recently, and
one of the most widely studied problems in this area is the identification of clusters, or …

The rapidly increasing volume of information contained in relational databases places a
strain on databases, performance, and maintainability: DBAs are under greater pressure …

We present a new result concerning the parallelisation of DBSCAN, a Data Mining algorithm
for density-based spatial clustering. The overall structure of DBSCAN has been mapped to a …

Data clustering has attracted a lot of research attention in the field of computational statistics
and data mining. In most related studies, the dissimilarity between two clusters is defined as …

Clustering is widely studied in data mining community. It is used to partition data set into
clusters so that intra-cluster data are similar and inter-cluster data are dissimilar. Different …

During the last two decades a wide variety of advanced methods for the visual exploration of
large data sets have been proposed. For most of these techniques user interaction has …

Clustering is a widely used knowledge discovery technique. It helps uncovering structures in
data that were not previously known. The clustering of large data sets has received a lot of …

本稿では, 代表的なデータ解析手法であるクラスタリングの最新手法を, 二回にわたって紹介する.
クラスタリングとは, 内的結合 (internalcohesion) と外的分離 (externalisolation) …

Business operations involve many factors and relationships and are modeled as complex
business process workflows. The execution of these business processes generates vast …

In this paper we devise an efficient algorithm for clustering market-basket data items. In view
of the nature of clustering market basket data, we devise in this paper a novel measurement …