Clustering can help aggregate the topology information and reduce the size of routing tables in a mobile ad hoc network (MANET). To achieve fairness and uniform energy consumption, each clusterhead should ideally support the same number of clustermembers. However, a MANET is a dynamic and complex system and its one important characteristic is the topology dynamics, that is, the network topology...

Community structure is one of the fundamental characteristics of complex networks. Many methods have been proposed for community detection. However, most of these methods are designed for static networks and are not suitable for dynamic networks that evolve over time. Recently, the evolutionary clustering framework was proposed for clustering dynamic data, and it can also be used for community ...

Document clustering is the widely researched area because of large amount of rich and dynamic information are available in world wide web. It is the application of cluster analysis to texual documents. There are different applications of document clustering include automatic document organization, data mining , topic extraction and filtering or fast information retrieval. The purpose of this su...

The general clustering algorithms do not guarantee optimality because of the hardness of the problem. Polynomial-time methods can find the clustering corresponding to the exact optimum only in special cases. For example, the dynamic programming algorithm can solve the one-dimensional clustering problem, i.e., when the items to be clustered can be characterised by only one scalar number. Optimal...

Text mining, in particular the clustering is mostly used by search engines to increase the recall and precision of a search query. The content of online websites (text, blogs, chats, news, etc.) are dynamically updated, never‐ theless relevant information on the changes made are not present. Such a scenario requires a dynamic text clustering method that operates without initial knowledge on a d...

Document clustering is a fundamental operation used in unsupervised document organization, automatic topic extraction and information retrieval. But most clustering technologies are limited in their application on the static document collection. Intelligence analysts are currently overwhelmed with tremendous amount of text information streams generated everyday. There is a lack of comprehensive...

Most clustering strategies have not changed considerably since their initial definition. The common improvements are either related to the distance measure used to assess dissimilarity, or the function used to calculate prototypes. Time-series clustering is no exception, with the Dynamic Time Warping distance being particularly popular in that context. This distance is computationally expensive...

To research rumor diffusion in social friend network, based on interests, a dynamic friend network is proposed, which has the characteristics of clustering and community, and a diffusion model is also proposed. With this friend network and rumor diffusion model, based on the zombie-city model, some simulation experiments to analyze the characteristics of rumor diffusion in social friend network...

Clustering is the process of grouping of data, where the grouping is established by finding similarities between data based on their characteristics. Such groups are termed as Clusters. Clustering is an unsupervised learning problem that group objects based upon distance or similarity. While a lot of work has been published on clustering of data on storage medium, little has been done about aut...