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Research Paper | Computer Science & Engineering | India | Volume 5 Issue 1, January 2016

Analysis of Frequent Item set Mining of Electronic Evidence using ISPO Tree based on Map/Reduce

Pranav Kumar Bhadane | R. V. Patil

Abstract: Association rules can mine the relevant evidence of computer crime from the massive data and association rules among data itemset, and further mine crime trends and connections among different crimes. They can help detect and leads case policies and prevent crime with given criterions. Frequent item set mining (FIM) plays a fundamental Associations, correlations and electronic evidence analysis area like many real-world data mining areas. FP-growth pattern of constant search is the most famous FIM algorithm. Incrementing data, time and space costs FP-growth will be mining algorithms bottleneck. Information and communication technologies in the world, with rapid advancements in the crimes committed are becoming technologically intensive. Use digital devices when crime, forensic examiners and practical frameworks which can pose as evidence to recover the data for analyzing the methods to adopt. Data Generation, Data Warehousing and Data Mining, are the three essential features involved in the investigation process. So that we proposed a novel parallelized algorithm called PISPO based on the cloud-computing framework MapReduce, which is widely used to cope with largescale data and captures both the content and state to be distributed to the changed and original of the transactions dataset to SPO tree.

Keywords: Data mining, ISPO tree algorithm, Minimum support, Threshold value, Electronic evidence, Map/Reduce approach, Frequent itemset mining

Edition: Volume 5 Issue 1, January 2016,

Pages: 139 - 144

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