This paper puts forward the concept of fractal company is in the process of online shopping. According to the characteristics of the network shopping, the paper establishes the mathematical model of fractal clustering, and uses the fractal dimension to describe and depict Fractal Company. Online shopping is actually the management of the entire supply chain. Based on similar structure of fracta...

Knowledge is power but for interrelated data, knowledge is often hidden in massive links in heterogeneous information networks. We explore the power of links at mining heterogeneous information networks with several interesting tasks, including link-based object distinction, veracity analysis, multidimensional online analytical processing of heterogeneous information networks, and rank-based cl...

As electronic commerce and knowledge economy environments proliferate, both individuals and organizations increasingly generate and consume large amounts of online information, typically available as textual documents. To manage this ever-increasing volume of documents, such individuals and organizations frequently organize their documents into categories that facilitate document management and...

To address the problem of the difficulty of traditional clustering methods to adapt to online clustering of streaming data and on the basis of the research on the evolutionary clustering method (ECM), this paper proposes a Davies-Bouldin index evolving clustering method for streaming data clustering (DBIECM). This method has improved the updating process of the clustering center and the radius ...

With great advances in the mobile technology and wireless communications, users expect to be online anytime anywhere. However, due to the high cost of being online, applications are still implemented as partially connected to the server. In many data-intensive mobile client/server frameworks, it is a daunting task to archive and index such a mass volume of complex data that are continuously add...

We propose an enhanced grid-density based approach for clustering high dimensional data. Our technique takes objects (or points) as atomic units in which the size requirement to cells is waived without losing clustering accuracy. For efficiency, a new partitioning is developed to make the number of cells ccepted 25 February 2011 vailable online 8 March 2011