research-article

Grouping game players using parallelized k-means on supercomputers

Authors:

Rabindra (Robby) Ratan,

Jay AlamedaAuthors Info & Claims

XSEDE '15: Proceedings of the 2015 XSEDE Conference: Scientific Advancements Enabled by Enhanced Cyberinfrastructure

Article No.: 10, Pages 1 - 7

https://doi.org/10.1145/2792745.2792755

Published: 26 July 2015 Publication History

Abstract

Grouping game players based on their online behaviors has attracted a lot of attention recently. However, due to the huge volume and extreme complexity in online game data collections, grouping players is a challenging task. This study has applied parallelized K-Means on Gordon, a supercomputer hosted at San Diego Supercomputer Center, to meet the computational challenge on this task. By using the parallelization functions supported by R, this study was able to cluster 120,000 game players into eight non-overlapping groups and speed up the clustering process by one to four times under the two- to eight-degree of parallelization. This study has systematically examined a number of factors which may affect the quality of the clusters and/or the performance of the clustering processes; those factors include degree of parallelism, number of clusters, data dimensions, and variable combinations. This study invented a method to identify the optimal clustering schema, which can choose the most discriminative features and create an appropriate number of clusters in K-Means clustering. Besides demonstrating the effectiveness of parallelized K-Means in grouping game players, this study also highlights some lessons learned for using K-Means on very large datasets and some experience on applying parallel processing techniques in intensive data analysis.

References

[1]

D. Ramirez-Cano, S. Colton, R. Baumgarten. Player Classification Using a Meta-Clustering Approach. In Proceeding of CGAT Conference, April 2010, Singapore.

[2]

D. Kerr, G. K. W. K. Chung. (2012). Identifying key features of student performance in educational video games and simulations through cluster analysis. Journal of Educational Data Mining, 4, 144--182.

[3]

A. Drachen, C. Bauckhage and R. Sifa. Introducing Clustering I: Behavioral Profiling for Game Analytics. Game Analytics Resources. May 2014.

[4]

R. Berkhin, R. A survey of clustering data mining techniques. In J. Kogan, C. Nicholas, & M. Teboulle (Eds.), Grouping multidimensional data (pp. 25--72). Springer, 2006

[5]

J. Han, M. Kamber and J. Pei. Data Mining, Concepts and Techniques, 3^rd ed., Morgan Kaufman, 2011.

Digital Library

[6]

C. Aggarwal and C. K. Reddy (eds.). Data Clustering: Algorithms and Applications. CRC Press, 2014

[7]

P. Xing, C. Kulikowski, I. Muchnik, I. Dubchak, D. M. Wolf, S. Spengler, M. Zorn. Analysis of ribosomal RNA sequences by combinatorial clustering. In Proceeding of International Conference on Intelligent System Molecular Biology. 1999:287--96

Digital Library

[8]

S. Reddy, A. Parker, J. Hyman, J. Burke, D. Estrin, M. Hansen. Image Browsing, Processing, and Clustering for Participatory Sensing: Lessons from a DietSense Prototype. In Proceeding of EmNets'07, Cork, Ireland.

Digital Library

[9]

K. Voges, N. Pope. A rough Cluster analysis of Shopping Orientation data. In Proceeding of ANZMAC 2003. Adelaide, Australia.

[10]

M. Mahajan, P. Nimbhorkar, and K. Varadarajan. The Planar k-Means Problem is NP-Nard. In Proceeding of WALCOM2009, pp. 274--285, 2009

Digital Library

[11]

G. K. Lockwood. Parallel Options for R. http://www.gennklockwood.com/di/R-para.php

[12]

D. Eddelbuettel. CRAN Task View: High-Performance and Parallel Computing with R. http://cran.r-project.org/web/views/HighPerformanceComputing.html

[13]

Y. D. Cai, B. C. Riedl, R. Ratan, C. Shen, and A. Picot. "FeatureSelector: an XSEDE-Enabled Tool for Massive Game Log Analysis". In Proceeding of XSEDE14, July, 2014, Atlanta, GA

Digital Library

[14]

S. Amershi, C. Conati, & H. Maclaren, H. Using feature selection and unsupervised clustering to identify affective expressions in educational games. In G. Rebolledo-Mendez & E. Martinez-Miron (Eds.), Proceedings of the Workshop on Motivational and Affective Issues at the 8th International Conference on Intelligent Tutoring Systems (pp. 21--28). Berlin, Heidelberg: Springer-Verlag.

[15]

G. K. W. K. & D. Kerr. (2012). A primer on data logging to support extraction of meaningful information from educational games: An example from Save Patch (CRESST Report 814). Los Angeles, CA, University of California, National Center for Research on Evaluation, Standards, and Student Testing.

Cited By

Li LYu TZhao WFu HWang CTan LYang GThomson J(2018)Large-scale hierarchical k-means for heterogeneous many-core supercomputersProceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis10.5555/3291656.3291674(1-11)Online publication date: 11-Nov-2018
https://dl.acm.org/doi/10.5555/3291656.3291674
Li LYu TZhao WFu HWang CTan LYang GThomson J(2018)Large-scale hierarchical k-means for heterogeneous many-core supercomputersProceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis10.1109/SC.2018.00016(1-11)Online publication date: 11-Nov-2018
https://dl.acm.org/doi/10.1109/SC.2018.00016
Wilkins-Diehr NSanielevici SAlameda JCazes JCrosby LPierce MRoskies R(2016)An Overview of the XSEDE Extended Collaborative Support ProgramHigh Performance Computer Applications10.1007/978-3-319-32243-8_1(3-13)Online publication date: 8-Apr-2016
https://doi.org/10.1007/978-3-319-32243-8_1

Index Terms

Grouping game players using parallelized k-means on supercomputers
1. Computing methodologies
  1. Parallel computing methodologies
    1. Parallel programming languages
2. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language types
        Parallel programming languages

Recommendations

Proficient Normalised Fuzzy K-Means With Initial Centroids Methodology

This article describes how data is relevant and if it can be organized, linked with other data and grouped into a cluster. Clustering is the process of organizing a given set of objects into a set of disjoint groups called clusters. There are a number ...
Initializing K-means Clustering Using Affinity Propagation
HIS '09: Proceedings of the 2009 Ninth International Conference on Hybrid Intelligent Systems - Volume 01

K-means clustering is widely used due to its fast convergence, but it is sensitive to the initial condition.Therefore, many methods of initializing K-means clustering have been proposed in the literatures. Compared with Kmeans clustering, a novel ...
Factorial and reduced K-means reconsidered

Factorial K-means analysis (FKM) and Reduced K-means analysis (RKM) are clustering methods that aim at simultaneously achieving a clustering of the objects and a dimension reduction of the variables. Because a comprehensive comparison between FKM and ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

XSEDE '15: Proceedings of the 2015 XSEDE Conference: Scientific Advancements Enabled by Enhanced Cyberinfrastructure

July 2015

296 pages

ISBN:9781450337205

DOI:10.1145/2792745

General Chair:
Gregory D. Peterson
National Institute of Computational Sciences

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

San Diego Super Computing Ctr: San Diego Super Computing Ctr
HPCWire: HPCWire
Omnibond: Omnibond Systems, LLC
SGI
Internet2
Indiana University: Indiana University
CASC: The Coalition for Academic Scientific Computation
NICS: National Institute for Computational Sciences
Intel: Intel
DDN: DataDirect Networks, Inc
DELL
CORSA: CORSA Technology
ALLINEA: Allinea Software
Cray
RENCI: Renaissance Computing Institute

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 July 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Science Foundation

Conference

XSEDE '15

Sponsor:

San Diego Super Computing Ctr
HPCWire
Omnibond
Indiana University
CASC
NICS
Intel
DDN
CORSA
ALLINEA
RENCI

XSEDE '15: Extreme Science Engineering Discovery Environment 2015 Conference

July 26 - 30, 2015

Missouri, St. Louis

Acceptance Rates

XSEDE '15 Paper Acceptance Rate 49 of 70 submissions, 70%;

Overall Acceptance Rate 129 of 190 submissions, 68%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
73
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 07 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Li LYu TZhao WFu HWang CTan LYang GThomson J(2018)Large-scale hierarchical k-means for heterogeneous many-core supercomputersProceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis10.5555/3291656.3291674(1-11)Online publication date: 11-Nov-2018
https://dl.acm.org/doi/10.5555/3291656.3291674
Li LYu TZhao WFu HWang CTan LYang GThomson J(2018)Large-scale hierarchical k-means for heterogeneous many-core supercomputersProceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis10.1109/SC.2018.00016(1-11)Online publication date: 11-Nov-2018
https://dl.acm.org/doi/10.1109/SC.2018.00016
Wilkins-Diehr NSanielevici SAlameda JCazes JCrosby LPierce MRoskies R(2016)An Overview of the XSEDE Extended Collaborative Support ProgramHigh Performance Computer Applications10.1007/978-3-319-32243-8_1(3-13)Online publication date: 8-Apr-2016
https://doi.org/10.1007/978-3-319-32243-8_1

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten