Clustering of retail stores in a distribution network with specific geographical limits plays an important and effective role in distribution and transportation costs reduction. In this paper, the relevant data and information for an established automotive spare-parts distribution and after-sales services company (ISACO) for a 3-year period have been analyzed. With respect to the diversity and ...

Clustering can be applied to many fields including data mining, statistical data analysis, pattern recognition, image processing etc. In the past decade, a lot of efficient and effective new clustering algorithms have been proposed, in which famous algorithms contributed from the database community are CLARANS, BIRCH, DBSCAN, CURE, STING, CLIGUE and WaveCluster. All these algorithms try to chal...

High dimensional data clustering arises naturally in a lot of domains, and have regularly presented a great deal with for usual data mining techniques. In this paper, presents an optimal perspective on the problem of Consensus Clustering in high-dimensional data. The proposed method called ―Fuzzy based and kernel mappings with Consensus Neighboring clustering (FKCNC)‖, which takes as key measur...

Spectral clustering is one of the most popular clustering approaches. Despite its good performance, it is limited in its applicability to large-scale problems due to its high computational complexity. Recently, many approaches have been proposed to accelerate the spectral clustering. Unfortunately, these methods usually sacrifice quite a lot information of the original data, thus result in a de...

The aim of collaborative clustering is to make different clustering methods collaborate, in order to reach at an agreement on the partitioning of a common dataset. As different clustering methods can produce different partitioning of the same dataset, finding a consensual clustering from these results is often a hard task. The collaboration aims to make the methods agree on the partitioning thr...

Clustering has become an increasingly important task in modern application domains. In many areas, e.g. when clustering complex objects, in distributed clustering, or when clustering mobile objects, due to technical, security, or efficiency reasons it is not possible to compute an “optimal” clustering. Recently a lot of research has been done on efficiently computing approximated clusterings. H...

Motif discovery from a set of sequences is a very important problem in biology. Although a lot of research has been done on computational techniques for (sequence) motif discovery, discovering motifs in a large number of sequences still remains challenging. We propose a novel computational framework that combines multiple computational techniques such as pairwise sequence comparison, clustering...

With large-scale wireless networks, the associated challenges of self-configuration of the network , self-healing in the case of any failure and self-maintenance assume significant importance. Without even one these features, the sensor network formed cannot be used any further. A possible solution to this problem is to use clustering of the nodes in the network. A geographical-radius based clu...

Annotating gene functions is a fundamental issue in the post-genomic era. A typical procedure for this issue is first clustering genes and then assigning functions of unknown genes by using known genes in the same cluster. A lot of genomic information are available for this issue, but two major types of data which can be measured for any genes are microarray expressions and sequences, both of w...