Advanced Data Mining and Applications: 9th International by Amjad Mahmood, Tianrui Li, Yan Yang, Hongjun Wang (auth.),

By Amjad Mahmood, Tianrui Li, Yan Yang, Hongjun Wang (auth.), Hiroshi Motoda, Zhaohui Wu, Longbing Cao, Osmar Zaiane, Min Yao, Wei Wang (eds.)

The two-volume set LNAI 8346 and 8347 constitutes the completely refereed lawsuits of the ninth foreign convention on complex information Mining and purposes, ADMA 2013, held in Hangzhou, China, in December 2013.
The 32 typical papers and sixty four brief papers awarded in those volumes have been conscientiously reviewed and chosen from 222 submissions. The papers incorporated in those volumes disguise the subsequent subject matters: opinion mining, habit mining, info circulate mining, sequential facts mining, net mining, snapshot mining, textual content mining, social community mining, type, clustering, organization rule mining, trend mining, regression, predication, characteristic extraction, identity, privateness upkeep, functions, and laptop learning.

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Additional resources for Advanced Data Mining and Applications: 9th International Conference, ADMA 2013, Hangzhou, China, December 14-16, 2013, Proceedings, Part II

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We call this technique DSC (Deterministic Sampling-based Clustering). As a case study we consider hierarchical clustering. Our empirical results show that DSC results in a speedup of more than an order of magnitude over exact hierarchical clustering algorithms when the data size is more than 6,000. Also, the accuracy obtained is excellent. In fact, on many datasets, we get an accuracy that is better than that of exact hierarchical clustering algorithms!. ) as well. Keywords: Clustering algorithms, Agglomerative hierarchical clustering, Center of gravity, Clustering efficiency.

Correlation Clustering [1] makes an exception, which is able to select k automatically. Moreover, this “model selection” property can be theoretically justified with a probabilistic interpretation [2], and theoretical analysis has been conducted for correlation clustering with error bounds derived [3]. Correlation clustering is a graph-based problem, where vertices correspond to the data points, and each edge (u,v) is labeled either “+” or “-” depending on whether vertices u and v are similar or not.

In principle, any L which satisfies (2) is legal. An example is to initialize L with 2I n×n − 1. e. the initial number of clusters) to a relatively large value. On the other hand, as the number of rows of initial L grows, the time complexity of the algorithm increases and the speed of convergence decreases. Here we describe a heuristic initialization of L based on the positive degree of vertices. First, all vertices are sorted in a list by the positive degree in descending order. Starting from the first vertex u in the list, a cluster indicator vector l is constructed by setting l(v) = 1 if vertex v has a positive relation with u and is currently in the list, l(v) = −1 otherwise, then the vertex u and v that l(v) = 1 are removed from the list.

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