In this study an approach for the prediction of wind power using nearest neighbour search and k-means clustering is studied and the results are compared to a prediction method based on artificial neural networks. The nearest neighbour search is combined with a population-based optimization algorithm for the selection of the input variables. This selection is done by a particle swarm optimizatio...

Data clustering is the process of partitioning a set of data objects into meaning clusters or groups. Due to the vast usage of clustering algorithms in many fields, a lot of research is still going on to find the best and efficient clustering algorithm. K-means is simple and easy to implement, but it suffers from initialization of cluster center and hence trapped in local optimum. In this paper...

Spatial joins are used to combine the spatial objects. The efficient processing depends upon the spatial queries. The execution time and input/output (I/O) time of spatial queries are crucial, because the spatial objects are very large and have several relations. In this article, we use several techniques to improve the efficiency of the spatial join; 1. We use R*-trees for spatial queries sinc...

Today, the development of a large scale access network internet/intranet has increased the amount of textual information available online/offline, where billions of documents have been created. In the last few years, bio inspired techniques which invaded the world of text-mining such, as clustering, represents a critical problem in the digital society especially over the world of information re...

Clustering represents the large datasets by a structured well defined number of clusters or prototypes. K-Means is a useful technique to data clustering which partitions the data into K-Clusters. However, the results of k-means algorithm are based on the selection of initial seeds and converge to local optimum solution. The Swarm Intelligence (SI) is an algorithm to apply many simple agents beh...

0957-4174/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.eswa.2010.07.112 ⇑ Corresponding author. E-mail addresses: [email protected] (H. I org (A. Abraham). 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,...

Conventional clustering algorithms utilize a single criterion that may not conform to the diverse shapes of the underlying clusters. We offer a new clustering approach that uses multiple clustering objective functions simultaneously. The proposed multiobjective clustering is a two-step process. It includes detection of clusters by a set of candidate objective functions as well as their integrat...

Fuzzy time series techniques are more suitable than traditional time series techniques in forecasting problems with linguistic values. Two shortcomings of existing fuzzy time series forecasting techniques are they lack persuasiveness in dealing with recurrent number of fuzzy relationships and assigning weights to elements of fuzzy rules in the defuzzification process. In this paper, a novel fuz...

Data clustering is a recognized data analysis method in data mining whereas K-Means is the well known partitional clustering method, possessing pleasant features. We observed that, K-Means and other partitional clustering techniques suffer from several limitations such as initial cluster centre selection, preknowledge of number of clusters, dead unit problem, multiple cluster membership and pre...