Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based

A new cluster algorithm for graphs called the Markov Cluster algorithm (MCL algorithm) is introduced.

Clustering, in data mining, is useful for discovering groups and identifying interesting distributions in the underlying data. Traditional clustering algorithms either favor clusters with spherical shapes and similar sizes, or are very fragile in the presence of outliers. We propose a new

of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. In this paper, we present the new clustering algorithm DBSCAN relying on a density-based notion of clusters which is designed to discover

, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts

Abstract—This paper presents a new clustering technique named as the Olary algorithm, which is suitable to cluster nominal data sets. This algorithm uses a new code with the name of the Olary code to transform nominal attributes into integer ones through a process named as the Olary transformation

with swarm–based algorithms is emerging as an alternative to more conventional clustering methods, such as k–means algorithm. This proposed approach mimics the clustering behavior observed in real ant colonies. As a case study, this paper focuses on the behavior of clustering procedures in this new approach

and their applications. SMC is sponsored by the Netherlands Organization for Scientific Research (NWO). CWI is a member of

to affect the clustering performance. Aiming at the above problem, NCA (New Clustering Algorithm) was proposed in this paper. NCA considers on intra-cluster and inter-cluster stability, which can improve cluster stability and reduce clustering overheads.

by continuously exchanging chemical cues. This phenomenon, known as "colonial closure", inspired us into developing a new clustering algorithm and then comparing it to a well-known method such as K-MEANS method. Our results show that our algorithm performs better than K-MEANS over artificial and real