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
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....
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, ...
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...
Techniques of cluster algorithms in data mining Data Mining and Knowledge Discovery, 6 (4) (2002), pp. 303-360 View in Scopus Google Scholar [2] P. Langfelder, B. Zhang, S. Horvath Defining clusters from a hierarchical cluster tree: the dynamic tree cut package for r ...
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 ...
To our knowledge, such clustering algorithms have not been previously applied to the study of longitudinal changes in people at risk for AD. The development of an interactive data mining multilayer clustering algorithm (MLC) has been spurred, in part, by recently introduced approaches of ...
The main contribution of the current study is to identify the characteristics of high and low risk banks based on the data mining clustering approach. This approach will be very useful for the government and regulators in achieving better results in banking consolidation....
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 ...