The goal of this article is to introduce two existing clustering approaches into the domain of ubiquitous knowledge discovery. First we demonstrate how horizontal collaborative clustering can be performed in a ubiquitous environment and discuss the ability of these two clustering techniques to cope with privacy constraints. Next, we illustrate how a particle swarm optimization driven version of...

This paper proposes the methods for solving the traveling salesman problems using clustering techniques and evolutionary methods. Gaussian mixer model and K-means clustering are two clustering techniques that are considered in this paper. The traveling salesman problems are clustered in order to group the nearest nodes in the problems. Then, the evolutionary methods are applied to each cluster....

Incorporating background knowledge in clustering problems has attracted wide interest. This knowledge can be represented as pairwise instance-level constraints. Existing techniques approach satisfaction of such constraints from a soft (discretionary) perspective, yet there exist scenarios for constrained clustering where satisfying as many constraints as possible. We present a new Lagrangian Co...

ABSTRACT Clustering algorithms have become increasingly important in handling and analyzing data. Considerable work has been done in devising e ective but increasingly speci c clustering algorithms. In contrast, we have developed a generalized framework that accommodates diverse clustering algorithms in a systematic way. This framework views clustering as a general process of iterative optimiza...

return maximization or risk minimization is goal in portfolio optimization based on mean variance theory. the structure of correlation matrices and individual variance of each asset are two main factors in optimization with risk minimization object. it’s necessary to use appropriate variance and correlation coefficient for time series with clustering volatilities feature, too. in this research,...

Clustering is a very well known technique in data mining. One of the most widely used clustering techniques is the kmeans algorithm. Solutions obtained from this technique depend on the initialization of cluster centers and the final solution converges to local minima. In order to overcome K-means algorithm shortcomings, this paper proposes a hybrid evolutionary algorithm based on the combinati...

There is huge amount of data available in health industry which is found difficult in handing, hence mining of data is necessary to innovate the hidden patterns and their relevant features. Recently, many researchers have devoted to the study of using data mining on disease diagnosis. Mining biomedical data is one of the predominant research area where evolutionary algorithms and clustering tec...

— Fuzzy clustering is an important problem which is the subject of active research in several real world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. Fuzzy clustering methods allow the objects to belong to several clusters simultaneously, with different degrees of membership. O...

For a decade swarm Intelligence is concerned with the design of intelligent systems by taking inspiration from the collective behaviors of social insects. Swarm Intelligence is a successful paradigm for the algorithm with complex problems. This paper focuses on the procedure of most successful methods of optimization techniques inspired by Swarm Intelligence: Ant Colony Optimization (ACO) and P...

This paper presents a variation of the Euclidean Traveling Salesman Problem (TSP), the Multiple Traveling Salesman Problem (MTSP), and compares a variety of evolutionary computation algorithms and paradigms for solving it. Techniques implemented, analyzed, and discussed herein with regard to MTSP include use of a neighborhood attractor schema (a variation on k-means clustering), the "shrink-wra...