Clustering is a widely used data mining task and a lot of constraint-based clustering methods have been developped. Our work focus on the problem of integrating constraintbased clustering in an inductive database system. We propose a new extension of SQL for constraint-based clustering. We present a concrete application in the context of microbiology.

The Self-Organizing Map (SOM) attracts attentions for clustering in these years. In our past study, we have proposed a method using simultaneously two kinds of SOMs whose features are different, namely, one self-organizes the area on which input data are concentrated, and the other self-organizes the whole of the input space. Further, we have applied this method to clustering of data including ...

Clustering, particularly text clustering, in data mining has been attracting a lot of attention of late. There have been conventional techniques like K-means, which involve parameters that can’t be easily estimated. With the emergence of density-based clustering algorithms which have significant advantages, a lot of attention has been devoted to them. OPTICS [1] is the latest and most sophistic...

In recent years, data clustering has been studied extensively and a lot of methods and theories have been achieved. However, with the development of the database and the popularity of Internet, a lot of new challenges such as Big Data and Cloud Computing lie in the research on data clustering. The paper presents a parallel k-means clustering algorithm based on MapReduce computing model of Hadoo...

To benefit from the large amounts of data, gathered in more and more application domains, analysis techniques like clustering have become a necessity. As their application expands, a lot of unacquainted users come into contact with these techniques. Unfortunately, most clustering approaches are complex and/or scenario specific, which makes clustering a challenging domain to access. In this demo...

Data clustering is the process of partitioning a set of data objects into meaning clusters or groups. Due to the vast usage of clustering algorithms in many fields, a lot of research is still going on to find the best and efficient clustering algorithm. K-means is simple and easy to implement, but it suffers from initialization of cluster center and hence trapped in local optimum. In this paper...

Facial expression is one of the main elements of nonverbal communication. Recently, a lot of effort has been made to automatically recognize and analyze these expressions from images and videos. However, most work in facial expression analysis is based on supervised approaches, and on individual subjects rather than a group. In classic clustering problems, the features of the data points are as...

Clustering method is divided into hierarchical clustering, partitioning clustering, and more. K-Means algorithm is one of partitioning clustering methods and is adequate to cluster a lot of data rapidly and easily. The problem is it is too dependent on initial centers of clusters and needs the time of allocation and recalculation. We compare random method, max average distance method and triang...

Clustering technology has received a lot of concern in many areas such as engineering, medicine, biology and data mining. Collecting data points is the purpose of clustering and the most common clustering technology is K-means algorithm. However, results of kmeans depend on the initial state and convergence to a local optimum is also its drawback. To overcome these drawbacks, many studies have ...