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	1. A High Performance Algorithm for Mining Frequent Patterns:LPS-Miner
	Xiaoyun Chen,Huiling Liu,Pengfei Chen,Longjie Li
	Computer Science and Technology 02 September 2008
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	Abstract:In this paper, we present a novel data-mining algorithm, called LPS-Miner, to mine the complete frequent patterns in the transaction database. LPS-Miner algorithm bases the pattern growth principle and uses two new data structures, LPS-FP-Tree (Light Partial-Support FP-Tree) and LPS-Forest (Light Partial-Support FP-Tree Forest) to present the database. LPS-FP-Tree is a variation of FP-Tree with lighter and unidirectional nodes and mining process depends on partial-support of the patterns. The algorithm adopts partition and divide-and-conquer strategies in maximum, which decomposes the mining task into a set of smaller tasks. The light data structure and the efficient memory management mechanism keep the memory usage stable and efficient. Moreover, other implementation-based optimizations, such as pruning and outputting-optimization, make the algorithm achieve high efficiency. We test our c++ implementation of this algorithm versus several other algorithms on four datasets. The experimental results show that our algorithm has better space and time efficiency.
	TO cite this article:Xiaoyun Chen,Huiling Liu,Pengfei Chen, et al. A High Performance Algorithm for Mining Frequent Patterns:LPS-Miner[OL].[ 2 September 2008] http://en.paper.edu.cn/en_releasepaper/content/23675


	2. Design and Implementation of the Spatial Index Structure QER+-tree
	Xiaoli Qi,Chen Xiaoyun ,Ma Jun
	Computer Science and Technology 18 August 2008
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	Abstract:QER+-tree, a new spatial index structure based on quad-tree, R-tree and R+-tree, is proposed. Its data structure, operation algorithms and experimental results are also stated. The main thought of QER+-tree is partitioning the whole index space to multi-levels using quad-tree. Then R-tree or R+-tree is used for indexing the subspaces. This way not only restrains the query space, but also decreases the overlap of index space. In addition, QER+-tree uses the reinserting mechanism while splitting nodes. It can better the trees’ structure. So QER+-tree has more superiority than R-tree and R+-tree.
	TO cite this article:Xiaoli Qi,Chen Xiaoyun ,Ma Jun . Design and Implementation of the Spatial Index Structure QER+-tree[OL].[18 August 2008] http://en.paper.edu.cn/en_releasepaper/content/23410


	3. Mining Correlations between Multi-Streams Based on Haar Wavelet
	Chen Anlong,Tang Changjie,Yuan Chang’an,Peng Jing ,Hu Jianjun
	Computer Science and Technology 13 September 2005
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	Abstract:In the application with multiple data streams, the correlation between data streams is very significant. The main contributions of this paper included: (1) Introduces the concept of total ordering with filter and compression by wavelets to describe streams. (2) Proposes the equivalence model to evaluate correlations between streams, including three theorems about the equivalence between wavelet coefficients and original data about computing correlation. (3) Designs anti-noise algorithm with sliding windows to compute correlation measure. (4) Gives extensive experiments on real data, which show that the local correlations are hardly affected by data with noise in the long windows, and that new algorithm has well filter on the streams with noise in the environment of short size windows.
	TO cite this article:Chen Anlong,Tang Changjie,Yuan Chang’an, et al. Mining Correlations between Multi-Streams Based on Haar Wavelet[OL].[13 September 2005] http://en.paper.edu.cn/en_releasepaper/content/2859


	4. An Optimization Model for Outlier Detection in Categorical Data
	Zengyou He
	Computer Science and Technology 31 March 2005
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	Abstract:The task of outlier detection is to find small groups of data objects that are exceptional when compared with rest large amount of data. Detection of such outliers is important for many applications such as fraud detection and customer migration. Most existing methods are designed for numeric data. They will encounter problems with real-life applications that contain categorical data. In this paper, we formally define the problem of outlier detection in categorical data as an optimization problem from a global viewpoint. Moreover, we present a local-search heuristic based algorithm for efficiently finding feasible solutions. Experimental results on real datasets and large synthetic datasets demonstrate the superiority of our model and algorithm.
	TO cite this article:Zengyou He. An Optimization Model for Outlier Detection in Categorical Data[OL].[31 March 2005] http://en.paper.edu.cn/en_releasepaper/content/1768

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