Abstract
Clustering is a traditional data mining problem that has attracted researchers from different disciplines because its solution can be applied to many useful problems in our daily life. Since the era of big data is coming, how to “reduce the computing time” of an “effective clustering algorithm” has been a promising research issue in recent years. Thus, this paper presents an effective clustering algorithm, by using the so-called searched information to determine later search directions, and then has it implemented on Spark to accelerate its response time for analyzing large-scale datasets. Simulation results show that the proposed algorithm provides a better result than the other clustering algorithms compared in this paper because it is less sensitive to the initial solutions. The simulation results further show that cloud computing platform is capable of enhancing the performance of the proposed algorithm.
| Original language | English |
|---|---|
| Journal | Computers in Human Behavior |
| DOIs | |
| Publication status | Accepted/In press - 2018 Jan 1 |
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All Science Journal Classification (ASJC) codes
- Arts and Humanities (miscellaneous)
- Human-Computer Interaction
- Psychology(all)
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A high-performance clustering algorithm based on searched experiences. / Tsai, Chun Wei; Ding, Yong Chun; Liu, Shi Jui; Chiang, Ming Chao; Yang, Chu-Sing.
In: Computers in Human Behavior, 01.01.2018.Research output: Contribution to journal › Article
TY - JOUR
T1 - A high-performance clustering algorithm based on searched experiences
AU - Tsai, Chun Wei
AU - Ding, Yong Chun
AU - Liu, Shi Jui
AU - Chiang, Ming Chao
AU - Yang, Chu-Sing
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Clustering is a traditional data mining problem that has attracted researchers from different disciplines because its solution can be applied to many useful problems in our daily life. Since the era of big data is coming, how to “reduce the computing time” of an “effective clustering algorithm” has been a promising research issue in recent years. Thus, this paper presents an effective clustering algorithm, by using the so-called searched information to determine later search directions, and then has it implemented on Spark to accelerate its response time for analyzing large-scale datasets. Simulation results show that the proposed algorithm provides a better result than the other clustering algorithms compared in this paper because it is less sensitive to the initial solutions. The simulation results further show that cloud computing platform is capable of enhancing the performance of the proposed algorithm.
AB - Clustering is a traditional data mining problem that has attracted researchers from different disciplines because its solution can be applied to many useful problems in our daily life. Since the era of big data is coming, how to “reduce the computing time” of an “effective clustering algorithm” has been a promising research issue in recent years. Thus, this paper presents an effective clustering algorithm, by using the so-called searched information to determine later search directions, and then has it implemented on Spark to accelerate its response time for analyzing large-scale datasets. Simulation results show that the proposed algorithm provides a better result than the other clustering algorithms compared in this paper because it is less sensitive to the initial solutions. The simulation results further show that cloud computing platform is capable of enhancing the performance of the proposed algorithm.
UR - http://www.scopus.com/inward/record.url?scp=85052936237&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85052936237&partnerID=8YFLogxK
U2 - 10.1016/j.chb.2018.08.038
DO - 10.1016/j.chb.2018.08.038
M3 - Article
AN - SCOPUS:85052936237
JO - Computers in Human Behavior
JF - Computers in Human Behavior
SN - 0747-5632
ER -