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Clustering is one of the most fundamental techniques in data mining. Although various algorithms have been proposed to solve clustering problem, the main difficulty left is the determination of optimal number for clusters. There are many algorithms trying to automatically select reasonable cluster number, such as Silhouette, Gap-test, Akiake Information Criterion, and Bayes Information Criterion....
K-means is one of the most significant clustering algorithms in data mining. It performs well in many cases, especially in the massive data sets. However, the result of clustering by K-means largely depends upon the initial centers, which makes K-means difficult to reach global optimum. In this paper, we developed a novel algorithm based on finding density peaks to optimize the initial centers for...
Nowadays, while we are enjoying the convenience brought by such a huge repository of online web information, we may come across difficulties in finding the web pages we want related to particular information we are searching for. Hence, it is essential to classify web documents to facilitate the search and retrieval of pages. Existing algorithms work well with a small quantity of web pages, whereas,...
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