Data clustering is one of the most important areas of research in data mining and knowledge discovery. Recent research in this area has shown that the best clustering results can be achieved using multi-objective methods. In other words, assuming more than one criterion as objective functions for clustering data can measurably increase the quality of clustering. In this study, a model with two ...

The detection and prevention of crime, in the past few decades, required several years of research and analysis. However, today, thanks to smart systems based on data mining techniques, it is possible to detect and prevent crime in a considerably less time. Classification and clustering-based smart techniques can classify and cluster the crime-related samples. The most important factor in the c...

In this work, a hierarchical ensemble of projected clustering algorithm for high-dimensional data is proposed. The basic concept of the algorithm is based on the active learning method (ALM) which is a fuzzy learning scheme, inspired by some behavioral features of human brain functionality. High-dimensional unsupervised active learning method (HUALM) is a clustering algorithm which blurs the da...

Clustering is the process of dividing a set of input data into a number of subgroups. The members of each subgroup are similar to each other but different from members of other subgroups. The genetic algorithm has enjoyed many applications in clustering data. One of these applications is the clustering of images. The problem with the earlier methods used in clustering images was in selecting in...

background: the two most frequently diagnosed cancers among women worldwide are breast and cervical cancers. the objective of the present study was to classify the different countries based on the death rates from sex specific cancers. methods : in this cross-sectional study, we used dataset regarding death rate from breast, cervical, uterine, and ovarian cancers in 190 countries worldwide repo...

in recent years stock exchange has become one of the most attractive and growing businesses in respect of investment and profitability. but applying a scientific approach in this field is really troublesome because of variety and complexity of decision making factors in the field. this paper tries to deliver a new solution for portfolio selection based on multi criteria decision making literatu...

nowadays, wireless sensor network has been of interest to investigators and the greatest challenge in this part is the limited energy of sensors. sensors usually are in the harsh environments and transit in these environments is hard and impossible and moreover the nodes use non- replaceable batteries. because of this, saving energy is very important. in this paper we tried to decrease hard and...

the ever severe dynamic competitive environment has led to increasing complexity of strategic decision making in giant organizations. strategy formulation is one of basic processes in achieving long range goals. since, in ordinary methods considering all factors and their significance in accomplishing individual goals are almost impossible. here, a new approach based on clustering method is pro...

although control charts are very common to monitoring process changes, they usually do not indicate the real time of the changes. identifying the real time of the process changes is known as change-point estimation problem. there are a number of change point models in the literature however most of the existing approaches are dedicated to normal processes. in this paper we propose a novel appro...