In recent years, the applications of Wireless Sensor Networks (WSNs) have grown enormously. In WSNs there is one mechanism used to enlarge the lifespan of network and provide more efficient functioning procedures that is clustering. Clustering is a process to subdivide the sensing field of sensor network into number of clusters. Each cluster selects a leader called cluster head. A cluster head ...

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...

Clustering as an important unsupervised learning technique is widely used to discover the inherent structure of a given data set. For clustering is depended on applications, researchers use different models to defined clustering problems. Heuristic clustering algorithm is an efficient way to deal with clustering problem defined by combining optimization model, but initialization sensitivity is ...

This paper describes a semi-automatic parking system, which can designate target position by recognizing parking lot markings. Driver is able to designate or refine the target position by drag&drop based novel user interface. Peak pair detection and clustering in Hough space recognize marking lines. Specially, one-dimensional filter in Hough space is developed to incorporate a priori knowledge ...

A fuzzy algorithm of web customers evaluation based on rough set is presented. Key attributes can be gotten through rough set. The evaluation from the data objects based on key attributes can reduce the data size and algorithm complexity. After Clustering analysis of customers, then the evaluation analysis will process to the clustering data. There are a lot of uncertain data in customer cluste...

With the evolution of IT technologies, large-scale graph data have lately become a growing interest. As a result, there are a lot of research results in large-scale graph analysis on Hadoop. The graph analysis based on Hadoop provides parallel programming models with data partitioning and contains iterative phases of MapReduce jobs. Therefore, the effectiveness of data partitioning depends on h...

However, in general, the parameters of density-based clustering algorithms are usually difficult to select. So, in order to make the density-based clustering algorithms more robust, the extension with fuzzy set theory has attracted a lot of attentions recently. The fuzzy neighborhood DBSCAN (FNDBSCAN) is a typical one with this idea. But FN-DBSCAN usually requires a time complexity of O(n2) whe...

Clustering graphs based on a comparison of the number of links within clusters and the expected value of this quantity in a random graph has gained a lot of attention and popularity in the last decade. Recently, Aldecoa and Maŕın proposed a related, but slightly different approach leading to the quality measure surprise, and reported good behavior in the context of synthetic and real world benc...