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In this paper we propose is an extension of kernel k-means clustering algorithm for symbolic interval data with aggregated kernel functions. To evaluate this method, experiments with synthetic interval data set was performed and we have been compared our method with a dynamic clustering algorithm with single adaptive distance. The evaluation is based on an external cluster validity index (corrected...
In this paper, we present a scalable evolutionary algorithm for clustering large and dynamic data sets, called Scalable Evolutionary Clustering with Self Adaptive Genetic Operators (Scalable ECSAGO). The proposed evolutionary clustering algorithm can adapt its genetic operators rate while the evolution leads to the optimal centers of the clusters. The sizes of the clusters are estimated using a hybrid...
Kernel k-means algorithms have recently been shown to perform better than conventional k-means algorithms in unsupervised classification. In this paper we present is an extension of kernel k-means clustering algorithm for symbolic interval data. To evaluate this method, experiments with synthetic and real interval data sets were performed and we have been compared our method with a dynamic clustering...
We present a new approach for parallel massive graph analysis of streaming, temporal data with a dynamic and extensible representation. Handling the constant stream of new data from health care, security, business, and social network applications requires new algorithms and data structures. We examine data structure and algorithm trade-offs that extract the parallelism necessary for high-performance...
Scaling the number of cores on processor chips has become the trend for current semiconduction industry (i.e. Intel/AMD many-core CPU, Nvida GPU etc). Current software development should take advantage of those multi-core platforms to achieve high performance. But it is a challenging task to develop parallel software on multiple processor because of the well known problems such as deadlock, load balancing,...
According to the characters of dynamic and SOM clustering algorithm, propose a novel clustering method, rough dynamic clustering based on grid-density algorithm (GDRDC). The algorithm contains initial clustering stages and precise adjustment stages. During switch from the first stage to second stage, according to rough sets idea, class kernel and freedom point sets base on grid-density are determined,...
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