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Discovering Weighted Calendar-Based Temporal Relationship Rules using Frequent Pattern Tree

Affiliations

  • Department of Computer Science and Engineering, BIT, Mesra: Ranchi, off-Campus: Noida, A-7, Sector 1, Noida – 201301, Uttar Pradesh, India

Abstract


The advent of data mining approach has brought many fascinating situations and several challenges to database community. The objective of data mining is to explore the unseen patterns in data, which are valid, novel, potentially subsidiary and ultimately understandable. The authorize and real-time transactional databases often show temporal feature and time validity life-span. Utilizing temporal relationship rule mining one may determine unusual relationship rules regarding different time-intervals. Some relationship rules may hold, through some intervals while not others and this may lead to subsidiary information. Using calendar mined patterns has already been projected by researchers to confine the time-validity relationships. However, when we consider the weight factor like utility of item in transactions and if we incorporate this weight factor in our model to mine then fascinating results of relationships come on time–variant data. This manuscript propose a narrative procedure to find relationship rule on time-variant-weighted data utilizing frequent pattern tree-hierarchical structures which give us a consequential benefit in expressions of time and memory-recollection utilization though including time and weight factor.

Keywords

Data-Mining, Temporal Association Rule-Mining, Temporal Data-Mining, Time-Weight-Carry Mining, Temporal-Weighted Relationship Rules, Weight-Carrying Transaction.

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References


  • Agrawal R, Mannila H, Srikant R, Toivonen H, Verkano I. MIT Press: Fast Discovery of Association Rules. 1996.
  • Srikent and Agrawal. Almaden: International Business Machine: Fast Algorithms for Mining Association Rules. 1994.
  • Agrawal and Srikant R. Mining Sequential Patterns. Proceeding IEEE International Conference on Database Engineering. 1995.
  • Juan M Ale and Gustavo Rossi. An approach to discovering temporal Association Rules. Association for Computing Machinery symposium on Applied Computing. 2000.
  • Book::Jiawei Han, Kamber and Jian Pei. The Morgan Kaufmann Publishers: Data Mining: Concepts and Techniques, 3rd Edition. 2011.
  • Lee Chang, Cheng-Ru Lin and Ming-Syan Chen. Slidingwindow filtering: An Efficient algorithm for Incremental Mining. Proceeding of Association for Computing Machinery International Conference on Information and Knowledge Management. 2001.
  • Xiaoxin and Jiawei. San Francisco: Classification based on predictive association rule. 2003.
  • Thabtah Abdelijaber, Peter C and Yonghong. Comparison of classification techniques for a personnel scheduling problem. International Business Information Management Conference. 2004.
  • Tunc and Dag, Generating classification association rules with modified Apriori. Spain: International Conference on Artificial Intelligence. 2006.
  • Book:: Ian H Witten, Eibe Frank and Mark A Hall. Morgan Kaufmann Publishers: Data mining: Practical Machine learning tools and techniques. 2000.
  • Wei Wang, Jiong Yang and Philip S Yu. Efficient mining of weighted association rules. Proceeding of 7th SIGKDD International Conference on Knowledge Discovery and Data Mining. 2000.
  • Coenen Frans and Leng Paul. An Evaluation of Approaches to Classification Rule Selection. Proceeding of 4th IEEE International Conference on Data Mining. 2004.
  • Ayad Ahmed, El-Makky Nagwa and Taha Yousry. Incremental Mining of Constrained Association Rules. Proceeding of SIAM Conference on Data Mining. 2001.
  • Weka:: Data Mining Software. Available from: http://www. cs.waikato.ac.nz/ml/weka.
  • Rymon R. Search Through Systematic Set Enumeration. Proceeding 3rd International Conference Principles of Knowledge and Reasoning. 1992.
  • Book:: Chen X, Petrounian I. Knowledge Discovery and Data Mining. Chapter 5-A Development Framework of Temporal data Mining. 2001.
  • Ozden B, Ramaswamy S and Silberschatz A. Cyclic Association Rule. Proceeding of 14th International conference on Data Engineering. 1998.
  • Jiawei Han, Jian Pei, Yiwen Yin and Runying Mao. Mining Frequent Pattern without Candidate Generation. Proceeding ACM-SIGMOD International Conference Management of Data. 2000.
  • Roddick JF, Hornsby K, Spiliopoulou M. An Updated Bibliography of Temporal, Spatial, and Spatio-temporal Data Mining Research, TSDM. 2000.
  • Hong Yao, Howard J Hamilton and Cory J Butz. A foundational Approach to Mining Item-set Utilities from Databases. Florida, USA: Proceedings of the 4th SIAM International Conference on Data Mining. 2004.

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