SCI-Tree: An Incremental Algorithm for Computing Support Counts of all Closed Intervals from an Interval Dataset
Dwipen Laskar1, Naba Jyoti Sarmah2, Anjana Kakoti Mahanta3
1Dwipen Laskar, Assistant Professor, Department of Computer Science, Gauhati University, Gauhati, India.
2Naba Jyoti Sarmah, Assistant Professor, Department of B.Voc.(IT), Nalbari Commerce College, Nalbari, Assam.
3Anjana Kakoti Mahanta, Professor, Department of Computer Science, Gauhati University, Gauhati, India.
Manuscript received on 07 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 August 2019 | PP: 233-242 | Volume-8 Issue-10, August 2019 | Retrieval Number: I8009078919/2019©BEIESP | DOI: 10.35940/ijitee.I8009.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Interval data mining is used to extract unknown patterns, hidden rules, associations etc. associated in interval based data. The extraction of closed interval is important because by mining the set of closed intervals and their support counts, the support counts of any interval can be computed easily. In this work an incremental algorithm for computing closed intervals together with their support counts from interval dataset is proposed. Many methods for mining closed intervals are available. Most of these methods assume a static data set as input and hence the algorithms are non-incremental. Real life data sets are however dynamic by nature. An efficient incremental algorithm called CI-Tree has been already proposed for computing closed intervals present in dynamic interval data. However this method could not compute the support values of the closed intervals. The proposed algorithm called SCI-Tree extracts all closed intervals together with their support values incrementally from the given interval data. Also, all the frequent closed intervals can be computed for any user defined minimum support with a single scan of SCI-Tree without revisiting the dataset. The proposed method has been tested with real life and synthetic datasets and results have been reported.
Keywords: Data mining, Interval data, Closed Interval, Support Count, Minimum Support.
Scope of the Article: Data mining