Abstract

Many real world data are closely associated with the interval of time and distance. Mining infrequent intervals from such data allows users to group transactions with less similarity while mining frequent interval allows user to group the transaction with a similarity above a certain measure. In [1], the notion of mining maximal frequent interval in either a discrete domain or continuous domain is introduced. This paper presents an effective minimal infrequent interval finding algorithm (MII) based on two maximal frequent interval finding techniques represented in [1] and [2] the proposed MII has been established to be effective both theoretically and experimentally.

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• Authors Biographies
• D. I. Mazumder, received his Master Degree in Mathematics from Gauhati University, India in 2003. He received his M.Tech in Information Technology, from Tezpur University, India in 2010. Currently He is working as a Lecturer of Mathematics in the Dept. of Information Technology in IBRI College of Technology, OMAN. His major Research interests include Data mining and Cryptography.
• D. k. Bhattacharyya, received his MCA from Dibrugarh University, India in 1991. He received PhD Degree in Computer Science from Tezpur University, India in 1999. Presently he is working as a Professor in the Department of Computer Science and Engg, Tezpur University, India. His Major research interests include Cryptography, Error correction/ detection, Content based image retrieval, Data mining and Network Security.
• M. Dutta, received his M.Sc. Degree in Physics from Delhi University in 1972. He received PhD Degree in Mathematics from IIT Kanpur in 1979. He also received M. S. in Computer Science from University of Houston in1982. Presently he is working as a professor in the Department of Computer Science and Engineering, Tezpur university, India. His major research area of interest are P=NP problem, Optimization and Data mining.