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3rd International Conference on Advanced Cloud and Big data (CBD)

作者： Li, Yun Xu, Jie Yuan, Yun-Hao Chen, Ling Yangzhou Univ Coll Informat Engn Yangzhou 225009 Jiangsu Peoples R China

vertical data structure is very important for closed frequent itemset mining. All closed frequent itemsets-can be found by simply using the operations of AND/OR. However, it consumes a large amount of storage space, especially in the case of large-size dataset. This paper proposes an algorithm for mining closed frequent itemsets based on a new vertical data structure. The proposed data structure is helpful to save storage space by using a multi-layer index. At the same time, numerous CPU and graphics processing unit can be employed in parallel to achieve high-efficiency computing. Especially when dealing with large datasets, the proposed algorithm can obtain a high-speed computing with the help of graphics processing unit. The improved vertical structure reduces the storage space of the data. The experimental results show that our proposed algorithm requires much less computation time than other related methods. Copyright (C) 2016 John Wiley & Sons, Ltd.

关键词： data mining closed frequent itemset vertical data structure GPU

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A new closed frequent itemset mining algorithm based on GPU and improved vertical structure

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2017年第6期29卷 1-12页

作者： Li, Yun Xu, Jie Yuan, Yun-Hao Chen, Ling Yangzhou Univ Coll Informat Engn Yangzhou 225009 Jiangsu Peoples R China

关键词： data mining closed frequent itemset vertical data structure GPU

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A new closed frequent itemsets mining algorithm based on GPU 3

A new closed frequent itemsets mining algorithm based on GPU

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3rd International Conference on Advanced Cloud and Big data (CBD)

作者： Li, Yun Xu, Jie Chen, Ling Yangzhou Univ Coll Informat Engn Yangzhou 225009 Jiangsu Peoples R China

ISBN: (纸本)9781467385374

vertical data format is an important structure in closed frequent itemsets mining. All closed frequent itemsets can be found by simply using the operations of "and" and "or". But it consumes huge amount of storage space, especially in the case of large size data set. This paper proposed an algorithm for mining closed frequent itemsets based on a new data structure deriving from the vertical format. This new data structure can contribute to save storage space by using a multi-layer index. When dealing with large data sets with the acceleration of GPU, this algorithm can obtain a high speed. Our experimental results show that our algorithm uses much less computation time than other similar methods.

关键词： data mining closed frequent item sets vertical data structure GPU

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SMART-TV: a fast and scalable nearest neighbor based classifier for data mining 06

SMART-TV: a fast and scalable nearest neighbor based classif...

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Proceedings of the 2006 ACM symposium on Applied computing

作者： Taufik Abidin William Perrizo North Dakota State University Fargo ND

ISBN: (纸本)9781595931085

K-nearest neighbors (KNN) is the simplest method for classification. Given a set of objects in a multi-dimensional feature space, the method assigns a category to an unclassified object based on the plurality of category of the k-nearest neighbors. The closeness between objects is determined using a distance measure, e.g. Euclidian distance. Despite its simplicity, KNN also has some drawbacks: 1) it suffers from expensive computational cost in training when the training set contains millions of objects; 2) its classification time is linear to the size of the training set. The larger the training set, the longer it takes to search for the k-nearest neighbors. In this paper, we propose a new algorithm, called SMART-TV (Small Absolute difference of Total Variation), that approximates a set of potential candidates of nearest neighbors by examining the absolute difference of total variation between each data object in the training set and the unclassified object. Then, the k-nearest neighbors are searched from that candidate set. We empirically evaluate the performance of our algorithm on both real and synthetic datasets and find that SMART-TV is fast and scalable. The classification accuracy of SMART-TV is high and comparable to the accuracy of the traditional KNN algorithm.

关键词： k-nearest neighbors classification vertical set squared distance vertical data structure

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