The evaluation of clustering algorithms is intrinsically difficult because of the lack of objective measures. Since the evaluation of clustering algorithms normally involves multiple criteria, it can be modeled as a multiple criteria decision making (MCDM) problem. This paper presents an MCDM-based approach to rank a selection of popular clustering algorithms in the domain of financial risk ana...

Many external and internal validity measures have been proposed in order to estimate the number of clusters in gene expression data but as a rule they do not consider the analysis of the stability of the groupings produced by a clustering algorithm. Based on the approach assessing the predictive power or stability of a partitioning, we propose the new measure of cluster validation and the selec...

Despite real progress in software engineering, the implementation of a successful measurement program in a software organization is still a serious challenge. Most of the problems are mainly due to the maturity level of the organization and to the credit given by the software engineering community for the software measure. Software measurement, like other disciplines, must be based on the measu...

Trajectory clustering has been used to very effectively in the detection of anomalous behavior in video sequences. A key point in trajectory clustering is how to measure the (dis)similarity between two trajectories. This paper deals with a new dissimilarity measure for trajectory clustering, giving the same importance to differences and similarities between the trajectories. Experimental result...

Short texts clustering is one of the most difficult tasks in natural language processing due to the low frequencies of the document terms. We are interested in analysing these kind of corpora in order to develop novel techniques that may be used to improve results obtained by classical clustering algorithms. In this paper we are presenting an evaluation of different internal clustering validity...

The recent banking crisis highlights the challenges faced in credit intermediation. New online peerto-peer lending markets offer opportunities to examine lending models that primarily cater to small borrowers and that generate more types of information on which to screen. This paper evaluates screening in a peer-to-peer market where lenders observe both standard financial information and soft, ...

Data clustering method is a process of putting similar data into groups. A clustering method partitions a data set into several groups such that the similarity within a group is larger than among groups. It has been playing an important role in solving many problems in image processing and pattern recognition. In this paper, a new method called hybrid clustering method is obtained by the most r...

This study uses the Propensity Score Matching to examine the income impact of different credit sources on accessed households in the Northern Mountains of Vietnam. Results show that overall rural credit serves an important role in improving household income with respect to total income, per capita income and nonfarm income. However, different credit affects recipients heterogeneously. Whereas a...