Temporal Data Mining: An Overview
Mohd. Shahnawaz1, Ashish Ranjan2, Mohd Danish3
1Mohd. Shahnawaz, Department of Computer Science & Engineering, Infinity Management & Engineering College, Sagar, India.
2Ashish Ranjan, Department of IT, OICL, New Delhi, India.
3Mohd Danish, Department of IT, NIC, New Delhi, India.
Manuscript received on October 06, 2011. | Revised Manuscript received on October 12, 2011. | Manuscript published on October 30, 2011 . | PP: 20-24 | Volume-1 Issue-1, October 2011. | Retrieval Number: A0099101111/2011©BEIESP
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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: To classify data mining problems and algorithms we used two dimensions: data type and type of mining operations. One of the main issue that arise during the data mining process is treating data that contains temporal information. The area of temporal data mining has very much attention in the last decade because from the time related feature of the data, one can extract much significant information which can not be extracted by the general methods of data mining. Many interesting techniques of temporal data mining were proposed and shown to be useful in many applications. Since temporal data mining brings together techniques from different fields such as databases, statistics and machine learning the literature is scattered among many different sources. In this paper, we present a survey on techniques of temporal data mining.
Keywords: Temporal Data; Temporal Data Mining; TDM Task; Temporal Sequence Mining.