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Volumn 41, Issue , 2008, Pages 33-42

Novel pruning based hierarchical agglomerative clustering for mining outliers in financial time series

Author keywords

Clustering; Computational finance; Data mining; Financial time series; High dimension; Outlier; Similarity search

Indexed keywords

ALGORITHMS; CLUSTERING ALGORITHMS; DATA MINING; FINANCE; FINANCIAL DATA PROCESSING; INDUSTRIAL MANAGEMENT; INFORMATION MANAGEMENT; INVESTMENTS; PLANNING; STRATEGIC PLANNING;

EID: 58849149385     PISSN: 17433517     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.2495/CF080041     Document Type: Conference Paper
Times cited : (4)

References (14)
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    • State-Of-The-Art Technologies for Securities Selection and Portfolio Management. Irwin Professional Publishing; Revised Edition January 1
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    • Dajun Wang, Paul J. Fortier, Howard E. Michel, and Theophano Mitsa. T- outlier and a novel dimensionality reduction framework in high dimensional time series financial data. The Second International Conference on Computational Finance and its Applications, June 2006
    • Dajun Wang, Paul J. Fortier, Howard E. Michel, and Theophano Mitsa. T- outlier and a novel dimensionality reduction framework in high dimensional time series financial data. The Second International Conference on Computational Finance and its Applications, June 2006
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    • Kenji Yamanishi and Jun-ichi Takeuchi. A Unifying Framework for Detecting Outliers and Change Points from Non-Stationary Time series Data. The eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Pages: 676-681, 2002.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.