Application Research of Modified K-Means Clustering Algorithm

Guo-li Liu (), You-qian Tan, Li-mei Yu, Jia Liu and Jin-qiao Gao
Additional contact information
Guo-li Liu: Hebei University of Technology
You-qian Tan: Hebei University of Technology
Li-mei Yu: Hebei University of Technology
Jia Liu: Hebei University of Technology
Jin-qiao Gao: Hebei University of Technology

Chapter Chapter 134 in The 19th International Conference on Industrial Engineering and Engineering Management, 2013, pp 1269-1280 from Springer

Abstract: Abstract This paper presents an efficient algorithm called K-harmonic means clustering algorithm with simulated annealing, for reducing the dependence of the initial values and overcoming to converge to local minimum. The proposed algorithm works by that K-harmonic means algorithm solves the problem that clustering result is sensitive to the initial valves and simulated annealing makes the clustering jump out of local optimal solution at each iteration patterns. The clustering result is verified by experiments on analyzing IRIS dataset. The school XunTong is application software that is convenient to communication between parents and teachers. This paper applies the new algorithm to analysis of dataset in School XunTong and finds the relationship of students’ achievement and the communication between parents and teachers. Finally, the result of classification guides the learning direction of students in universities and cultivates to students.

Keywords: K-Harmonic means; Simulated annealing; Local minimum; School XunTong (search for similar items in EconPapers)
Date: 2013
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-38427-1_134

Ordering information: This item can be ordered from
http://www.springer.com/9783642384271

DOI: 10.1007/978-3-642-38427-1_134

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().