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2003-2017 全部
为您找到包含“data mining”的内容共482

Zengyou He

he data mining process consists of a series of steps ranging from data cleaning, data selection and

2005-01-28

国家863项目(2003AA4Z2170/2003AA4l3021

Harbin Institute of Technology

#Computer Science and Technology#

0评论(0 分享(0)

He Zengyou,Xu Xiaofei,Deng Shengchun

lustering is a widely used technique in data mining applications for discovering patterns in

2005-09-06

国家重点基础研究发展规划项目(2002AA413310

Harbin institute of technology,Harbin institute of technology,Harbin institute of technology

#Computer Science and Technology#

0评论(0 分享(0)

Qiu ZhenYu,Peng Lijuan

processing speed is slow. We use data mining technology to search the best optimal data according to SVM

2008-04-17

Institute of Image & Graphics, College of Computer Science, Sichuan University,Institute of Image & Graphics, College of Computer Science, Sichuan University

#Computer Science and Technology#

0评论(0 分享(0)

Zengyou He,Xiaofei Xu,Shengchun Deng

Clustering categorical data is an integral part of data mining and has attracted much attention

2005-11-04

863(Grant No. 2003AA4Z3370

Department of Computer Science and Engineering, Harbin Institute of Technology,Department of Computer Science and Engineering, Harbin Institute of Technology,Department of Computer Science and Engineering, Harbin Institute of Technology

#Computer Science and Technology#

0评论(0 分享(0)

Zuo Jie,Tangchangjie,Lichaun,Chen au-long,Yuan Chang-an

Time series prediction is a typical and significant task in data mining, which has been widely

2004-06-28

博士点基金(20020610007

computer school, sichaun university,computer school, sichaun university,computer school- sichaun university,computer school- sichaun university,computer school- sichaun university

#Mechanics#

0评论(0 分享(0)

zengyou he,Xiaofei Xu,Shengchun Deng,Bin Dong

customer data using data mining techniques. In particular, it introduces the C2S system that performs this

2004-10-29

Department of Computer Science and Engineering, Harbin Institute of Technology,Department of Computer Science and Engineering Harbin Institute of Technology,Department of Computer Science and Engineering Harbin Institute of Technology,Department of Computer Science and Engineering Harbin Institute of Technology

#Mechanics#

0评论(0 分享(0)

zengyou he

The task of outlier detection is to find small groups of data objects that are exceptional when compared with rest large amount of data. In [38], the problem of outlier detection in categorical data is defined as an optimization problem and a local-search heuristic based algorithm (LSA) is presented. However, as is the case with most iterative type algorithms, the LSA algorithm is still very time-consuming on very large datasets. In this paper, we present a very fast greedy algorithm for mining outliers under the same optimization model. Experimental results on real datasets and large synthetic datasets show that: (1) Our algorithm has comparable performance with respect to those state-of-art outlier detection algorithms on identifying true outliers and (2) Our algorithm can be an order of magnitude faster than LSA algorithm.

2005-07-28

Harbin Institute of Technology

#Computer Science and Technology#

0评论(0 分享(0)

zengyou he

requent pattern (itemset) mining in transactional databases is one of the most well-studied problems in data mining. One obstacle that limits the practical usage of frequent pattern mining is the extremely large number of patterns generated. Such a large size of the output collection makes it difficult for users to understand and use in practice. Even restricting the output to the border of the frequent itemset collection does not help much in alleviating the problem. In this paper we address the issue of overwhelmingly large output size by introducing and studying the following problem: mining top-k approximate frequent patterns. The union of the power sets of these k sets should satisfy the following conditions: (1) including itemsets with larger support as many as possible and (2) including itemsets with smaller support as few as possible. An integrated objective function is designed to combine these two objectives. Consequently, we derive the upper bounds on objective function an

2005-03-18

Harbin Institute of Technology

#Computer Science and Technology#

0评论(0 分享(0)

zengyou he

In this paper, the traditional k-modes clustering algorithm is extended by weighting attribute value matches in dissimilarity computation. The use of attribute value weighting technique makes it possible to generate clusters with stronger intra-similarities, and therefore achieve better clustering performance. Experimental results on real life datasets show that these value weighting based k-modes algorithms are superior to the standard k-modes algorithm with respect to clustering accuracy.

2007-01-12

Harbin Institute of Technology

#Computer Science and Technology#

0评论(0 分享(0)

杨莉,金俐伶,赵芳芳

2006-12-18

进行地理空间数据挖掘是一个复杂的工程,一个有效的系统的建立涉及到许多方面的问题,本文就实现思路、数据组织、数据接口、聚类算法优化、信息系统不确定等方面的问题进行了研究,得出一些有益的解决办法,具有一定的理论意义和实际参考价值。

辽宁工程技术大学测绘与地理科学学院,辽宁工程技术大学测绘与地理科学学院,辽宁工程技术大学测绘与地理科学学院

#测绘科学技术#

0评论(0 分享(0)