T Ramakrishnudu

Work place: Dept. of CSE, National Institute of Technology, Warangal, 506004, India

E-mail: trk@nitw.ac.in

Website:

Research Interests: Computer systems and computational processes, Distributed Computing, Data Mining, Data Structures and Algorithms

Biography

T Ramakrishnudu was born on June 01, 1980. He received B.Tech (Computer Science and Engineering) degree from Jawaharlal Nehru Technological University, Hyderabad, India in 2001 and M.Tech (Computer Science and Technology) from Andhra University, Visakhapatnam, India in 2005. Currently he is working as an Assistant Professor in the department of Computer Science and Engineering in National Institute of Technology Warangal, India.  His research interests include Data Mining, Distributed Data Mining and Big Data Analytics. He is a member in IEEE, ACM and Computer Society of India.

Author Articles
Mining Interesting Infrequent Itemsets from Very Large Data based on MapReduce Framework

By T Ramakrishnudu R B V Subramanyam

DOI: https://doi.org/10.5815/ijisa.2015.07.06, Pub. Date: 8 Jun. 2015

Mining frequent and infrequent itemsets from a given dataset is the most important field of data mining. When we mine frequent and infrequent itemsets simultaneously, infrequent itemsets become very important because there are many valued negative association rules in them. Mining frequent Itemset is highly expensive, if the minimum threshold is low, whereas mining infrequent itemsets is highly expensive, if the minimum threshold is high. When the dataset size is very large, both memory usage and computational cost of mining infrequent items is very expensive. In addition, single processor’s memory and CPU resources are not enough to handle very large datasets. Parallel and distributed computing are effective approaches to handle large datasets. In this paper we proposed a method based on Hadoop-MapReduce model, which can handle massive datasets in mining infrequent itemsets. Experiments are performed on 8 node cluster with a synthetic dataset. The performance study shows that the proposed method is efficient in handling very large datasets.

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