Temporal data is time-based data and real-time applications involve such data. Clustering time-based data improves the efficiency of the frequent itemsets obtained. An existing method for improving the clusters used Ant Colony Optimization (ACO) in FCM. The major drawbacks of ACO are that the ...
One of the objectives of spatio-temporal data mining is to analyze such datasets to exploit moving objects that travel together. Naturally, the moving objects in a cluster may actually diverge temporarily and congregate at certain timestamps. Thus, there are time gaps among moving object clusters...
Hsu, WAuvil, LRedman, TTcheng, DM. Welge, W. H. Hsu, L. S. Auvil, T. M. Redman and D. Tcheng, High-Performance Knowledge Discovery and Data Mining Systems Using Workstation Clusters, in 12th National Conference on High Performance Networking and Computing (SC99), 1999....
High performance is a key issue in data mining or in image rendering. Advances in clustering technology have led to high-availability and load-balancing clusters. Clustering is now used for mission-critical applications such as web and FTP servers. For example, Google uses an ever-growing ...
Clusters are also playing a greater role in business. High performance is a key issue in data mining or in image rendering. Advances in clustering technology have led to high-availability and load-balancing clusters. Clustering is now used for mission-critical applications such as web and FTP se...
Clusters are also playing a greater role in business. High performance is a key issue in data mining or in image rendering. Advances in clustering technology have led to high-availability and load-balancing clusters. Clustering is now used for mission-critical applications such as web and FTP se...
Rudolph Techniques of cluster algorithms in data mining Data Mining and Knowledge Discovery, 6 (4) (2002), pp. 303-360 View in ScopusGoogle Scholar [2] P. Langfelder, B. Zhang, S. Horvath Defining clusters from a hierarchical cluster tree: the dynamic tree cut package for r Bioinformatics...
3. According to this plateau, the galaxy cluster can be identified in the dendrogram. This procedure provides a quantitative operational standards of structure extraction in dendrogram. Serra and Diaferio (2013) systematically tested this method by using data from large scale cosmological simulations, ...
For example, the gene expression data record the expression levels of a set of thousands of genes under hundreds of experimental conditions. Traditional clustering algorithms fail to efficiently find clusters of genes that demonstrate similar expression levels in all conditions due to such a high ...
Ma, P.C.H., Chan, K.C.C. (2008). Mining Gene Expression Patterns for the Discovery of Overlapping Clusters. In: Marchiori, E., Moore, J.H. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2008. Lecture Notes in Computer Science, vol 4973. Sp...