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With the market competition being increasingly intensive, it is necessary for company to carry out one-to-one marketing to consumers. Therefore, the ability to predict consumer's consumption behavior basing on data mining has become a key source of competitive advantage for company. In this paper, we propose a novel algorithm, which bases on ant colony optimization (ACO) to cluster consumer's consumption...
Under the research and analysis on different types of clustering algorithms, focus on the limitation of the Jarvis-Patrick algorithm and other clustering algorithm based on SNN density, a new clustering algorithm is proposed in this paper, that is, Improved Clustering algorithm based on Local Gathering Features. The paper gives the definition of the Gathering Features during the procedure of the clustering,...
Community related applications such as instant messengers or file-sharing tools enjoy a great popularity in the Internet. But, the forming of communities is unprompted and the joining of users is based on their interesting. The opportunity to spread malicious-programs or purloin user's information is provided to malicious users. So, a clustering algorithm based on trust is presented to form a safe...
The existing clustering algorithms based on grid are analyzed, and the clustering algorithms based on grid have the advantages of dealing with high dimensional data and high efficiency. However, traditional algorithms based on grid are influenced greatly by the granularity of grid partition. An incremental clustering algorithm based on grid, which is called IGrid, is proposed. IGrid has the advantage...
The optimization of search results has always been the research hotspot in the area of search engine. More concretely, topic partition by clustering proved to be a good way. However, the clusters, some of which still contain a lot of documents, implicitly limit the users' retrieval speed. Meanwhile we find that the information of documents' features have good effects on the document ranking. To address...
According to the characteristics of edge betweenness in the complex network, if the betweenness of an edge is relative lower, a pair of nodes connected by that edge should be in the same community. An algorithm for detecting community structure is proposed based on this observation. After grouping nodes according to edge betweenness, some nodes not assigned yet to any community in the network are...
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