An Approach to Nearest Neighboring Search for Multi-dimensional Data

被引：0

作者：

Shi, Yong ^{[1
]}

Zhang, Li ^{[2
]}

Zhu, Lei ^{[3
]}

机构：

[1] Kennesaw State Univ, Dept Comp Sci & Informat Syst, 1000 Chastain Rd, Kennesaw, GA 30144 USA

[2] Eastern Michigan Univ, Dept Comp Sci, Ypsilanti, MI 48197 USA

[3] Clayton State Univ, Dept Informat Technol, Morrow, GA 30260 USA

来源：

INTERNATIONAL JOURNAL OF FUTURE GENERATION COMMUNICATION AND NETWORKING | 2011年 / 4卷 / 01期

关键词：

K-nearest search; multi-dimensional data; obstacles;

D O I：

暂无

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

Finding nearest neighbors in large multi-dimensional data has always been one of the research interests in data mining field. In this paper, we present our continuous research on similarity search problems. Previously we have worked on exploring the meaning of K nearest neighbors from a new perspective in PanKNN [20]. It redefines the distances between data points and a given query point Q, efficiently and effectively selecting data points which are closest to Q. It can be applied in various data mining fields. A large amount of real data sets have irrelevant or obstacle information which greatly affects the effectiveness and efficiency of finding nearest neighbors for a given query data point. In this paper, we present our approach to solving the similarity search problem in the presence of obstacles. We apply the concept of obstacle points and process the similarity search problems in a different way. This approach can assist to improve the performance of existing data analysis approaches.

引用

页码：23 / 37

页数：15

共 25 条

[11] Estivill-Castro V., 2001, Temporal, Spatial, and Spatio-Temporal Data Mining. First International Workshop, TSDM 2000. Revised Papers (Lecture Notes in Artificial Intelligence Vol.2007), P133
[12] Estivill-Castro V., 2000, P 5 INT C GEOC, P23
[13] Fagin R., 2003, EFFICIENT SIMILARITY
[14] Gionis A, 1999, PROCEEDINGS OF THE TWENTY-FIFTH INTERNATIONAL CONFERENCE ON VERY LARGE DATA BASES, P518
[15] Hinneburg A, 1999, PROCEEDINGS OF THE TWENTY-FIFTH INTERNATIONAL CONFERENCE ON VERY LARGE DATA BASES, P506
[16] Ng R.T., 1994, P VLDB, P144
[17] Seidl T., 1998, SIGMOD Record, V27, P154, DOI 10.1145/276305.276319
[18] Shi Y., 2008, 4 INT C DAT MIN DMIN
[19] Spatial clustering in the presence of obstacles
Tung, AKH
Hou, J
Han, JW
[J]. 17TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING, PROCEEDINGS, 2001, : 359 - 367
[20] Wang X, 2003, LECT NOTES ARTIF INT, V2637, P563

← 1 2 3 →