Data mining clusters

Data mining can fulfill various different tasks such as classification, clustering and similarity detection, prediction, estimation, or description retrieval, which are described in Sections 9.8.1-9.8.5. 

Accelerated approaches to data mining strategies19 involve sophisticated computer-driven data processing20 such as the X-hitting algorithm21 and cluster analyses.22 Each facilitates the rapid identification of active compounds while providing a measure of chemical diversity and novelty, thus reducing the discovery timeline. 

Bases, Santiago, Chile, 1994, pp. 144-155. Efficient and Effective Clustering Methods for Spatial Data Mining. 

International Conference on Management of Data, Seattle, WA, 1998, pp. 94—105. Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications. 

Conference on Knowledge Discovery and Data Mining, R. Agrawal, P. Stolorz, and G. Piatetsky-Shapiro, Eds., AAAI Press, Menlo Park, CA, 1998, pp. 194-198. Initialization of Iterative Refinement Clustering Algorithms. 

II. Complex dynamical behavior in clusters and proteins, and data mining to extract information on dynamics. 

In the second part, we collect contributions concerning dynamical processes in complex systems such as clusters and proteins. Here, we also include those ideas related to data mining, since this topic is an indispensable part of the studies on dynamics of macromolecules. 

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