A Modeling Methodology for Analysis of Dynamic Systems Using Heuristic Search and Design of Interface for CRM (휴리스틱 탐색을 통한 동적시스템 분석을 위한 모델링 방법과 CRM 위한 인터페이스 설계)

전진호; Gye Sung Lee (이계성)

A Modeling Methodology for Analysis of Dynamic Systems Using Heuristic Search and Design of Interface for CRM

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2009, 14(4), pp.201-209
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science

전진호 ¹, Gye Sung Lee ²

¹관동대학교
²단국대학교

Accredited

ABSTRACT

Most real world systems contain a series of dynamic and complex phenomena. One of common methods to understand these systems is to build a model and analyze the behavior of them. A two-step methodology comprised of clustering and then model creation is proposed for the analysis on time series data. An interface is designed for CRM(Customer Relationship Management) that provides user with 1:1 customized information using system modeling. It was confirmed from experiments that better clustering would be derived from model based approach than similarity based one. Clustering is followed by model creation over the clustered groups, by which future direction of time series data movement could be predicted. The effectiveness of the method was validated by checking how similarly predicted values from the models move together with real data such as stock prices.

KEYWORDS

Time series data, Clustering, Model-based, CRM

Citation status

* References for papers published after 2023 are currently being built.

[confproc] J. Alon / 2003 / Discovering cluster in motion time-series data / Proceedings of Computer Vision and Pattern Recognition

[confproc] F. Porrkli / 2004 / Clustering variable length sequences by eigenvector decomposition using hmm / Intermational workshop on statistical pattern recognition(SPR 2004)

[journal] T. Okuda / 1976 / A method for the correction of garbled words based on the levenshtein metric / IEEE Transaction on Computers C25(2) : 172~177

[book] A. K. Jain / 1988 / Algorithms for clustering data / Prentice Hall

[confproc] J. Lin / 2002 / Finding motifs in time series," In the Workshop on Temporal Data Mining / at the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

[confproc] L. Rabiner / 1989 / A tutorial on Hidden Markov Models and selected applications in speech recognition / Proc. of IEEE : 257~286

[journal] 전진호 / 2006 / A Study for Determining the Best Number of Clusters on Temporal Data / 한국콘텐츠학회 논문지 / 한국콘텐츠학회 6(1) : 23~30

[confproc] T. Kosaka / 1995 / Speaker-independent phone modeling based on speaker-dependent hmm's composition and clustering / In Proceeding of the Twentieth International Conference on Acoustics, Speech, and Signal Processing : 441~444

[confproc] L. R. Rabiner / 1989 / Hmm clustering for connected word recognition / In Proceedings of the Fourteenth International Conference on Acoustics, Speech, and Signal Processing : 405~408

[confproc] E. Dermatas / 1996 / Algorithm for clustering continuous density hmm by recognition error / IEEE Transactions on Speech and Audio Processing : 231~234

[journal] D. Heckerman / 1995 / A tutorial on learning with Bayesian Network / Machine Learning 20 : 197~243

KJCKorea
Journal Central

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65