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Vimal Kumar, D.
- A Survey on Effective Relevance Feedback Methods for Web Information Retrieval
Abstract Views :199 |
PDF Views:4
Authors
Affiliations
1 Nehru Arts and Science College, T.M. Palayam, Coimbatore, IN
2 Department of Computer Science, Nehru Arts and Science College, T.M. Palayam, Coimbatore, IN
1 Nehru Arts and Science College, T.M. Palayam, Coimbatore, IN
2 Department of Computer Science, Nehru Arts and Science College, T.M. Palayam, Coimbatore, IN
Source
Data Mining and Knowledge Engineering, Vol 10, No 1 (2018), Pagination: 7-8Abstract
In current scenario, information retrieval is the tedious process due to its vast collection of interconnected hyperlinked documents available in the web. In general, there are several tools and search engines are available to retrieve information’s from web repositories. According to the users query, the search engines are retrieving hundreds and thousands of web links. Several contents are not useful and irrelevant to the user query. However, there are some popular search engines providing appropriate results to the user query, the user need to give the query in a proper manner. This leads to several information retrieval problems. In order to improve the retrieval efficiency, RF (Relevance Feedback) methods are introduced. The relevance feedbacks are categorized into three types, one is implicit, explicit and pseudo feedback. With the use of Relevance feedback and user query management, the information retrieval can be performed effectively. This paper provides a detailed summary of relevance feedback techniques and gives several future directions.Keywords
Data Mining, Information’s Retrieval, Relevance Feedback.
References
- Buttcher, Stefan, Charles LA Clarke, and Gordon V. Cormack. Information retrieval: Implementing and evaluating search engines. Mit Press, 2016.
- Salton, Gerard, and Chris Buckley. "Improving retrieval performance by relevance feedback." Readings in information retrieval 24, no. 5 (1997): 355-363.
- Hiemstra, Djoerd. "Information retrieval models." Information Retrieval: searching in the 21st Century (2009): 2-19.
- Aji, Ablimit, Yu Wang, Eugene Agichtein, and Evgeniy Gabrilovich. "Using the past to score the present: Extending term weighting models through revision history analysis." In Proceedings of the 19th ACM international conference on Information and knowledge management, pp. 629-638. ACM, 2010.
- Blanco, Roi, and Paolo Boldi. "Extending BM25 with multiple query operators." In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval, pp. 921-930. ACM, 2012.
- Yang, Yi, Feiping Nie, Dong Xu, Jiebo Luo, Yueting Zhuang, and Yunhe Pan. "A multimedia retrieval framework based on semi-supervised ranking and relevance feedback." IEEE Transactions on Pattern Analysis and Machine Intelligence 34, no. 4 (2012): 723-742.
- Raman, Karthik, Paul N. Bennett, and Kevyn Collins-Thompson. "Toward whole-session relevance: exploring intrinsic diversity in web search." In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, pp. 463-472. ACM, 2013.
- Mbarek, Rabeb, Mohamed Tmar, and Hawete Hattab. "A new relevance feedback algorithm based on vector space basis change." In International Conference on Intelligent Text Processing and Computational Linguistics, pp. 355-366. Springer, Berlin, Heidelberg, 2014.
- Melucci, Massimo. "Relevance Feedback Algorithms Inspired By Quantum Detection." IEEE Transactions on Knowledge and Data Engineering 28, no. 4 (2016): 1022-1034.
- Survey on Algorithms Applied in Pattern Mining
Abstract Views :237 |
PDF Views:2
Authors
Affiliations
1 Nehru Arts and Science College, T.M.Palayam, Coimbatore, IN
2 Department of Computer Science, Nehru Arts and Science College, T.M.Palayam, Coimbatore, IN
1 Nehru Arts and Science College, T.M.Palayam, Coimbatore, IN
2 Department of Computer Science, Nehru Arts and Science College, T.M.Palayam, Coimbatore, IN
Source
Data Mining and Knowledge Engineering, Vol 10, No 1 (2018), Pagination: 8-11Abstract
Data mining is a collection of techniques to extract hidden and potentially useful information from large databases of various business domains. For identifying the interesting patterns and co-relation and to get benefits from the repository data, Association Rule Mining (ARM) methods are used. Pattern recognition is a major challenge within the field of data mining and knowledge discovery. In this paper, a range of widely used algorithms are analyzed for finding frequent patterns with the purpose of discovering how these algorithms can be used to obtain frequent patterns over large transactional databases. This has been presented in the form of a comparative study of the following algorithms: Apriori algorithm, Frequent Pattern (FP) Growth algorithm, Rapid Association Rule Mining (RARM), ECLAT algorithm and Associated Sensor Pattern Mining of Data Stream (ASPMS) frequent pattern mining algorithms. The paper also focuses on each of the algorithm’s strengths and weaknesses for finding patterns in different transactional dataset.References
- Sourav S. Bhowmick Qiankun Zhao, "Association Rule Mining: A Survey," Nanyang Technological University, Singapore.
- Jiawei Han • Hong Cheng • Dong Xin • Xifeng Yan, "Frequent pattern mining: current status and future Directions," Data Mining Knowl Discov, vol. 15, no. I, p. 32, 2007.
- Iqbal Gondal and Joarder Kamruzzaman Md. Mamunur Rashid, "Mining Associated Sensor Pattern for data stream of wireless networks," in PM2HW2N '13, Spain, 2013.
- Chistopher.T, PhD Saravanan Suba, "A Study on Milestones of Association Rule Mining," International Journal of Computer Applications, p. 7, June 2012.
- WeeKeong, YewKwong Amitabha Das, "Rapid Association Rule Mining," in Information and Knowledge Management, Atlanta, Georgia, 2001, pp. 474-481.