Data Mining in Sequential Pattern for Asynchronous Periodic Patterns
Data Mining is becoming an increasingly important tool to transform enormous data into useful information. Mining periodic patterns in temporal dataset plays an important role in data mining and knowledge discovery tasks. This paper presents, design and development of software for sequential pattern mining for asynchronous periodic patterns in temporal database. Comparative study of various algorithms on sequential pattern mining for asynchronous periodic patterns is also carried out by taking artificial and real life database of glossary shop. The proposed system will be based on optimization of Efficient Mining of Asynchronous Periodic Pattern Algorithm (EMAP), which will be implemented for efficient mining of asynchronous periodic patterns in large temporal database.
Keywords: Sequential patterns, Temporal dataset, Knowledge discovery, Asynchronous Periodic patterns
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ABOUT THE AUTHOR
Thodeti Srikanth
Thodeti Srikanth received his Master of Computer Applications degree from Kakatiya University, Andhra Pradesh, INDIA in 2004. He is pursuing Ph.D. (Computer Science) from Dravidian University, Andhra Pradesh, INDIA.
Thodeti Srikanth
Thodeti Srikanth received his Master of Computer Applications degree from Kakatiya University, Andhra Pradesh, INDIA in 2004. He is pursuing Ph.D. (Computer Science) from Dravidian University, Andhra Pradesh, INDIA.
