The k_means clustering algorithm has very extensive application. paper gives out_in based on density . combines distance with data to adapt distribution. It can effectively solve the of data. Out_in reduce distorition by move out and in. Simulation results show that is more effective than algorithm.

Text clustering is one of the difficult and hot research fields in the internet search engine research. A new text clustering algorithm is presented based on Kmeans and Self-Organizing Model (SOM). Firstly, texts are preprocessed to satisfy succeed process requirement. Secondly, the paper improves selection of initial cluster centers and cluster seed selection methods of K-means to improve the ...

Telecommunication Companies use data mining techniques to maintain good relationships with their existing customers and attract new customers and identifying profitable/unprofitable customers. Clustering leads to better understanding of customer and its results can be used to definition and decision-making for promotional schemes. In this study, we used the 999-customer purchase records in Sout...

A well-known clustering algorithm is K-means. This algorithm, besides advantages such as high speed and ease of employment, suffers from the problem of local optima. In order to overcome this problem, a lot of studies have been done in clustering. This paper presents a hybrid Extended Cuckoo Optimization Algorithm (ECOA) and K-means (K), which is called ECOA-K. The COA algorithm has advantages ...

In order to realize automatic disease diagnosis and provide related information for disease prediction and control timely and accurately, the identification and diagnosis of grape downy mildew and grape powdery mildew was conducted based on image recognition technologies. The method based on K_means clustering algorithm was used to implement unsupervised segmentation of the disease images. Fift...