In this paper, a new method is proposed for solving the data clustering problem using Cat Swarm Optimization (CSO) algorithm based on chaotic behavior. The problem of data clustering is an important section in the field of the data mining, which has always been noted by researchers and experts in data mining for its numerous applications in solving real-world problems. The CSO algorithm is one ...

The focal inadequacy of modern wireless sensor networks is constrained energy resources of sensors. Therefore, Energy efficient clustering techniques and routing algorithms must be deployed to intensify the durability of wireless sensor networks. The research of an improved particle swarm optimization method for wireless sensor networks is presented in this paper. In order to conquer the inadeq...

This research addresses the design and development of an incremental software architecture recovery and evaluation environment using data mining techniques. The environment is interactive and provides: pattern-based architectural recovery using a query language and approximate graph pattern matching; optimization clustering; partitioning; and view-based architectural design evaluation. These te...

In this paper, a new method is proposed for solving the data clustering problem using Cat Swarm Optimization (CSO) algorithm based on chaotic behavior. The problem of data clustering is an important section in the field of the data mining, which has always been noted by researchers and experts in data mining for its numerous applications in solving real-world problems. The CSO algorithm is one ...

  The clustering problem under the criterion of minimum sum of squares is a non-convex and non-linear program, which possesses many locally optimal values, resulting that its solution often falls into these trap and therefore cannot converge to global optima solution. In this paper, an efficient hybrid optimization algorithm is developed for solving this problem, called Tabu-KM. It gathers the ...

We derive real{time global optimization methods for several clustering optimization problems commonly used in unsupervised texture segmentation. Speed is achieved by exploiting the image neighborhood relation of features to design a multiscale optimization technique, while accuracy and global optimization properties are gained using annealing techniques. Coarse grained cost functions are derive...

Data mining is the process of extracting previously unknown and valid information from large databases. Clustering is an important data analysis and data mining method. It is the unsupervised classification of objects into clusters such that the objects from same cluster are similar and objects from different clusters are dissimilar. Data clustering is a difficult unsupervised learning problem ...

Enormous amount of assorted information is available on the web. Clustering is one of the techniques to deal with huge amount of information. Clustering partitions a data set into groups where data objects in each group should exhibit large measure of resemblance. Objects with high resemblance measure should be placed in a cluster (intra cluster). Resemblance between the objects of different cl...

Sensor networks is among the fastest growing technologies that have the potential of changing our lives drastically. These collaborative, dynamic and distributed computing and communicating systems will be self organizing. They will have capabilities of distributing a task among themselves for efficient computation. There are many challenges in implementation of such systems: energy dissipation...