Integration of bacterial foraging  with K-means for Web user session clustering

Computer Engineering and Applications ›› 2012, Vol. 48 ›› Issue (36): 121-124.

Previous Articles Next Articles

Integration of bacterial foraging with K-means for Web user session clustering

LING Haifeng1，2, WANG Hao1，2

1.School of Management, Hefei University of Technology, Hefei 230009, China
2.Key Lab of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei 230009, China

Online:2012-12-21 Published:2012-12-21

细菌觅食算法与K-means结合的Web用户会话聚类

凌海峰1，2，王浩1，2

1.合肥工业大学管理学院，合肥 230009
2.过程优化与智能决策教育部重点实验室，合肥 230009

Abstract

Abstract: Web user session clustering is an NP-hard problem of the e-commerce field. The purpose is to discover user access patterns of behavior. The difficulty of the problem is that large-scale Web session clustering, and each session is indicated for the high-dimensional vector. This paper presents a type of clustering algorithm combining bacterial foraging algorithm with K-means algorithm, using the well-known data set to test their effectiveness, and the Web session clustering. Compared with the popular clustering algorithm, the experimental results show that the algorithm is efficient and has better performance.

Key words: Web usage mining, bacterial foraging optimization, K-means algorithm, Web session clustering, e-business

摘要： Web用户会话聚类是电子商务领域的NP-难问题，目的是发现相似的用户访问行为模式。该问题难度在于对大规模的Web会话进行聚类，且每个会话都表示为高维向量。提出一种细菌觅食算法和K-means相结合的优化算法，用知名的数据集测试其有效性。对Web会话进行聚类，与流行的聚类算法进行比较，实验结果显示该算法高效且性能更优。

关键词: Web使用挖掘, 细菌觅食优化, K-means算法, 会话聚类, 电子商务

LING Haifeng1，2, WANG Hao1，2. Integration of bacterial foraging with K-means for Web user session clustering[J]. Computer Engineering and Applications, 2012, 48(36): 121-124.

凌海峰1，2，王浩1，2. 细菌觅食算法与K-means结合的Web用户会话聚类[J]. 计算机工程与应用, 2012, 48(36): 121-124.

[1]	PAN Chengsheng, ZHANG Bin, LYU Yana, DU Xiuli, QIU Shaoming. K-Means Text Clustering Based on Improved Gray Wolf Optimization Algorithm [J]. Computer Engineering and Applications, 2021, 57(1): 188-193.
[2]	WANG Zilong, LI Jin, SONG Yafei. Improved K-means Algorithm Based on Distance and Weight [J]. Computer Engineering and Applications, 2020, 56(23): 87-94.
[3]	ZHANG Zhen, LI Haofang, LI Mengzhou. Research on YOLO Algorithm in Abnormal Security Images [J]. Computer Engineering and Applications, 2020, 56(21): 187-193.
[4]	LI Feng, LI Mingxiang, ZHANG Yujing. Partial Iterative Fast K-means Clustering Algorithm [J]. Computer Engineering and Applications, 2020, 56(13): 63-71.
[5]	HUANG Jianqiong, GUO Wenlong. Hybrid Particle Swarm Optimization and Improved Bacterial Foraging Optimization to Classify Imbalanced Data [J]. Computer Engineering and Applications, 2020, 56(10): 171-178.
[6]	WANG Jianren, MA Xin, DUAN Ganglong. Improved K-means Clustering k-Value Selection Algorithm [J]. Computer Engineering and Applications, 2019, 55(8): 27-33.
[7]	CHEN Qinghu, ZHOU Xiaodan, YAN Yuchen. Recognition of print file based on character image segmentation [J]. Computer Engineering and Applications, 2018, 54(7): 170-175.
[8]	LIU Xuejing, HE Yichao, WU Congcong, CAI Xiufeng. Research on bacterial foraging optimization algorithm for discounted {0-1} knapsack problem [J]. Computer Engineering and Applications, 2018, 54(2): 155-162.
[9]	ZHOU Benjin, TAO Yizheng, JI Bin, XIE Yonghui. Optimizing k-means initial clustering centers by minimizing sum of squared error [J]. Computer Engineering and Applications, 2018, 54(15): 48-52.
[10]	LIN Shijie1，2, DONG Chen1，2, CHEN Mingzhi1，2, ZHANG Fan1，2, CHEN Jinghui1，2. Summary of new group intelligent optimization algorithms [J]. Computer Engineering and Applications, 2018, 54(12): 1-9.
[11]	WANG Binyu1, LIU Wenfen2, HU Xuexian1, WEI Jianghong1. Research on text clustering for selecting initial cluster center based on Cosine distance [J]. Computer Engineering and Applications, 2018, 54(10): 11-18.
[12]	WANG Zhaofeng, SHAN Ganlin . k-means based method for dynamically selecting DBSCAN algorithm parameters [J]. Computer Engineering and Applications, 2017, 53(3): 80-86.
[13]	BAI Shuren1，2, CHEN Long2. Particle clustering algorithm with adaptive K values [J]. Computer Engineering and Applications, 2017, 53(16): 116-120.
[14]	QIU Yunfei, ZHAO Bin, LIN Mingming, WANG Wei. Improved K-means clustering algorithm combined semantic similarity of short text [J]. Computer Engineering and Applications, 2016, 52(19): 78-83.
[15]	OU Hui, XIA Zhuoqun, WU Zhiwei. Rough k-means clustering algorithm based on improved manifold distance [J]. Computer Engineering and Applications, 2016, 52(14): 84-89.

Integration of bacterial foraging with K-means for Web user session clustering

细菌觅食算法与K-means结合的Web用户会话聚类

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics