A NOVEL APPROACH FOR EXCEPTIONAL PHENOMENA KNOWLEDGE DETECTION AND ANALYSIS BY DATA MINING

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Hajigol Yazdi Elahe; ABESSI MASOUD; FAKHRZAD MOHAMMAD BAGHER; HOSEINI NASAB HASAN

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Journal: SIGNAL AND DATA PROCESSING Year:2017 | Volume:14 | Issue:1 SERIAL 31) Page(s): 15-28

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Title

A NOVEL APPROACH FOR EXCEPTIONAL PHENOMENA KNOWLEDGE DETECTION AND ANALYSIS BY DATA MINING

Author(s)

Hajigol Yazdi Elahe | ABESSI MASOUD | FAKHRZAD MOHAMMAD BAGHER | HOSEINI NASAB HASAN | Issue Writer Certificate

Keywords

DATA MININGQ1

EXCEPTIONAL PHENOMENAQ1

ABNORMALITY THEORYQ1

BOTTOM-UP LEARNING APPROACHQ1

E-RISE ALGORITHMQ1

INFORMATION THEORYQ1

Abstract

Learning logic of exceptions is a substantial challenge in DATA MINING and knowledge discovery. EXCEPTIONAL PHENOMENA detection takes place among huge records in a database which contains a large number of normal records and a few of exceptional ones. This is important to promote the confidence to a limited number of exceptional records for effective learning. In this study, a new approach based on the ABNORMALITY THEORY, information and information granulation theories are presented to detect exceptions and recognize their behavioral patterns. The efficiency of the proposed method was determined by using it to detect exceptional stocks from Iran stock market in a 30-month-period and learn their exceptional behavior. The proposed Enhanced-RISE algorithm (E-RISE) as a BOTTOM-UP LEARNING APPROACH was implemented to extract the knowledge of normal and exceptional behavior. The extracted knowledge was utilized to design an expert system based on the proposed ABNORMALITY THEORY to predict new exceptions from 6022 stocks. The superior findings show the results of this proposed approach in EXCEPTIONAL PHENOMENA detection, is in accordance with experts' opinions.

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APA: Copy

Hajigol Yazdi, Elahe, ABESSI, MASOUD, FAKHRZAD, MOHAMMAD BAGHER, & HOSEINI NASAB, HASAN. (2017). A NOVEL APPROACH FOR EXCEPTIONAL PHENOMENA KNOWLEDGE DETECTION AND ANALYSIS BY DATA MINING. SIGNAL AND DATA PROCESSING, 14(1 SERIAL 31) ), 15-28. SID. https://sid.ir/paper/160735/en

Vancouver: Copy

Hajigol Yazdi Elahe, ABESSI MASOUD, FAKHRZAD MOHAMMAD BAGHER, HOSEINI NASAB HASAN. A NOVEL APPROACH FOR EXCEPTIONAL PHENOMENA KNOWLEDGE DETECTION AND ANALYSIS BY DATA MINING. SIGNAL AND DATA PROCESSING[Internet]. 2017;14(1 SERIAL 31) ):15-28. Available from: https://sid.ir/paper/160735/en

IEEE: Copy

Elahe Hajigol Yazdi, MASOUD ABESSI, MOHAMMAD BAGHER FAKHRZAD, and HASAN HOSEINI NASAB, “A NOVEL APPROACH FOR EXCEPTIONAL PHENOMENA KNOWLEDGE DETECTION AND ANALYSIS BY DATA MINING,” SIGNAL AND DATA PROCESSING, vol. 14, no. 1 SERIAL 31) , pp. 15–28, 2017, [Online]. Available: https://sid.ir/paper/160735/en

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