Int. J. of Business Intelligence and Data Mining   »   2018 Vol.13, No.1/2/3

 

 

Title: Frequent pattern sub-space clustering optimisation algorithm for data mining from large database

 

Authors: T. Sheik Yousuf; M. Indra Devi

 

Addresses:
Department of Computer Science Engineering, Mohammed Sathak Engineering College, Kilakarai, Ramanathapuram, TamilNadu, 623-503, India
Department of Computer Science Engineering, Kamaraj College of Engineering and Technology, Virudhunagar, TamilNadu, India

 

Abstract: Data mining environment gives a quick response to the user by fast and correctly pick-out the item from the large database is a very challenging task. Previously, multiple algorithms were proposed to identify the frequent item since they are scanning database at multiple times. To overcome those problems, we proposed Rehashing based Apriori Technique in which hashing technology is used to store the data in horizontal and vertical formats. Rehash Based Apriori uses hashing function to reduce the size of candidate item set and scanning of database, eliminate non-frequent items and avoid hash collision. After finding frequent item sets, perform level wise subspace clustering. We instigate generalised self organised tree based (GSTB) mechanism to adaptively selecting root to construct the path from the cluster head to neighbours when constructing the tree. Our experimental results show that our proposed mechanisms reduce the computational time of overall process.

 

Keywords: sub-space clustering; generalised self-organised tree-based cluster head selection; GSTB.

 

DOI: 10.1504/IJBIDM.2017.10004686

 

Int. J. of Business Intelligence and Data Mining, 2018 Vol.13, No.1/2/3, pp.221 - 246

 

Available online: 03 Nov 2017

 

 

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