Titlebook: Advanced Data Mining and Applications; 10th International C Xudong Luo,Jeffrey Xu Yu,Zhi Li Conference proceedings 2014 Springer Internatio

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Seeing China: Shaw and His Contemporariesace. To solve these problems, an improved Apriori algorithm based on compression matrix is proposed. The improvement ideas of this algorithm are as follows: (1) reducing the times of scanning matrix set during compressing by adding two arrays to record the counts of 1 in the row and column; (2) mini

使成核

Seeing China: Shaw and His Contemporariesbase. Several efficient algorithms have been proposed for this task. But few can handle items with negative unit profits despite that such items occurs in many real-life transaction databases. Mining HUIs in a database where items have positive and negative unit profits is a very computationally exp

CHART

Bernard Shaw’s Bridges to Chinese Cultureng execution times and huge memory consumption. To address this issue, concise representations of HUIs have been proposed. However, no concise representation of HUIs has been proposed based on the concept of . despite that it provides several benefits in many applications. In this paper, we incorpor

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Bernard Shaw and His Contemporariescture and offers greater understanding, but networks change over time. Their constituent communities change, and the elements of those communities change over time as well. By performing event analysis, the evolutions of communities are abstracted in order to see structure in the dynamic change over

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Shaw and the Rise of Reverse Snobbery, structure, and most global community detection methods often suffer from the computational inefficiency. Local community detection aims at finding a community structure starting from a seed vertex without global information. In this article, we propose a Local Community Extraction algorithm (LCE) t

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,Bernard Shaw’s Plays—Not in This Family,with invariant moments. Specifically, the proposed hashing firstly converts the input image into a normalized image by interpolation, filtering and color space conversion. Then it divides the normalized image into overlapping blocks and extracts invariant moments of blocks to form a feature matrix.

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