Abstract
Due to information overload on the Internet a large number of systems have been developed for extracting user behavior. This paper presents mining of Frequent Itemsets and refinement of usage clusters for web based applications. Here a particular case is under consideration where sessions in a cluster are in abundance, consequently leading to a very large number of not-so interesting recommendations for the user. To solve such problems we intend to refine clusters on the basis of Weighted Frequent Itemsets that in turn help to generate improved quality refined clusters. In the proposed work, Frequent Itemsets are sets of web pages that occur in sessions more than a given threshold known as the minimum support. Motivation for adapting Frequent Itemsets for refinement is the demand of dimensionality reduction. Experimental results show that the cluster quality using the proposed approach is better than the existing approaches (DBS, 2011 and HITS, 2010). After getting refined clusters the same can be used for number of applications such as Web Personalization, improvement in Web Site Structure, Analysis of Users’ Online Behavior and the services of a Recommender System.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Mobasher: Discovery of aggregate usage profiles for web personalization. WebKDD, Boston (2009)
Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowledge-Based Systems 46, 109–132 (2013)
Ziegler, C.N.: On Recommender Systems. In: Ziegler, C.N. (ed.) Social Web Artifacts for Boosting Recommenders. SCI, vol. 487, pp. 11–22. Springer, Heidelberg (2013)
Berkhin, P.: Survey of clustering data mining techniques. Springer, Heidelberg
Flake, G., Lawrence, S., Giles, C.L., Coetzee, F.: Self-organization and identification of Web Communities. IEEE Computer 35(3) (2002)
Castellano, G., Fanelli, A.M., Mencar, C., Torsello, M.A.: Similarity based Fuzzy clustering for user profiling. In: Proceedings of International Conference on Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM (2007)
Xu, R., Wunsch, D.: Survey of clustering algorithms. IEEE Trans. Neural Networks 16(3), 645–678 (2005)
Mobasher, B., Cooley, R., Srivastava, J.: Automatic Personalization based on Web Usage Mining. Communications of the ACM 43(8), 142–151 (2000)
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, SIGMOD 1993, p. 207 (1993)
Nock, R., Nielsen, F.: On Weighting Clustering. IEEE Transactions and Pattern Analysis and Machine Intelligence 28(8), 1223–1235 (2006)
Baldi, P., Frasconi, P., Smyth, P.: Modeling the Internet and the Web. Wiley (2003)
Chakrabarti, S.: Mining the Web. Morgan Kaufmann Publishers (2003)
Banerjee, A., Ghosh, J.: Click stream clustering using weighted longest common subsequences. In: Proceedings of the Web Mining Workshop at the 1st SIAM Conference on Data Mining (2001)
Cadez, H.D., Meek, C., Smyth, P., White, S.: Model-based clustering and visualization of navigation patterns on a Web site. Data Mining and Knowledge Discovery 7(4), 399–424 (2003)
Fu, Y., Sandhu, K., Shih, M.Y.: Clustering of Web users based on access patterns. In: Proceedings of WEBKDD (1999)
Hay, B., Vanhoof, K., Wetsr, G.: Clustering navigation patterns on a Website using a sequence alignment method. In: Proceedings of 17th International Joint Conference on Artificial Intelligence, Seattle, Washington, USA (2001)
Wang, W., Zaane, O.R.: Clustering Web sessions by sequence alignment. In: Proceedings of the 13th International Workshop on Database and Expert Systems Applications, pp. 394–398. IEEE Computer Society, Washington, DC (2002)
Shahabe, C., Zarkesh, A.M., Abidi, J., Shah, V.: Knowledge discovery from user’s web-page navigation. In: Proceedings Seventh IEEE International Workshop on Research Issues in Data Engineering (RIDE), pp. 20–29 (1997)
Eiron, N., McCurley, K.S.: Untangling compound documents on the Web. In: Proceedings of the Fourteenth ACM Conference on Hypertext and Hypermedia (2003)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: VLDB 1994, pp. 487–499 (1994)
Greco, G., Greco, S., Zumpano, E.: Web communities: models and algorithms. Journal of World Wide Web 7(1), 58–82 (2004)
Cheng, J., Ke, Y., Ng, Q.: A survey on algorithms for mining frequent itemsets over data streams. Knowledge and Information Systems 16, 1–27 (2008)
Srivastava, J., Cooley, R., Deshpande, M., Tan, P.: Web usage mining: discovery and applications of usage patterns from Web data. ACM SIGKDD Explorations Newsletter 1(2), 12–23 (2000)
Munk, M., Kapusta, J., Svec, P.: Data preprocessing evaluation for web log mining: reconstruction of activities of a web visitor. Procedia Computer Science 1(1), 2273–2280 (2010)
Kosala, R., Blockeel, H.: Web Mining Research: A Survey. ACM SIGKDD Explorations 2(1), 1–15 (2000)
Liao, S.H., Chu, P.H., Hsiao, P.Y.: Data mining techniques and applications – A decade review from 2000 to 2011. Expert Systems with Applications 39(12), 11303–11311 (2011)
Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recognition Letters 31(8), 651–666 (2010), Award Winning Papers from the 19th International Conference on Pattern Recognition (ICPR) (2010)
Boyinbode, O., Le, H., Takizawa, M.: A survey on clustering algorithms for wireless sensor networks. International Journal of Space-Based and Situated Computing 1(2-3), 130–136 (2011)
Prusiewicz, A., Zięba, M.: Services Recommendation in Systems Based on Service Oriented Architecture by Applying Modified ROCK Algorithm. In: Networked Digital Technologies Communications in Computer and Information Science, vol. 88, pp. 226–238. Springer (2010)
Karypis, G., Han, E.H., Kumar, V.: Chameleon: hierarchical clustering using dynamic modeling. Computer 32(8), 68–75 (1999)
Davies, D.L., Bouldin, D.W.A.: Cluster Separation Measure. Pattern Analysis and Machine Intelligence. IEEE Transactions PAMI-1(2), 224–227 (1979)
Chen, M.S., Han, J., Yu, P.S.: Data mining: an overview from a database perspective. Knowledge and Data Engineering 8(6), 866–883 (1996)
Goethals, B.: Frequent Set Mining. In: Data Mining and Knowledge Discovery Handbook, pp. 377–397. Springer (2005)
Berkhin, P.: A Survey of Clustering Data Mining Techniques. In: Grouping Multidimensional Data, pp. 25–71. Springer (2006)
Borgelt, C.: An implementation of the FP-growth algorithm. In: Proceedings of the 1st International Workshop on Open Source Data Mining: Frequent Pattern Mining Implementations, pp. 1–5. ACM (2005)
Maulik, U., Bandyopadhyay, S.: Performance evaluation of some clustering algorithms and validity indices. Pattern Analysis and Machine Intelligence 24(12), 1650–1654 (2002)
Cooley, R., Mobasher, B., Srivastava, J.: Data Preparation for mining World Wide Web Browsing Patterns. In: Knowledge and Information Systems, pp. 1–25. Springer (1999)
Cormode, G., Hadjieleftheriou, M.: Methods for finding frequent items in data streams. The VLDB Journal 19, 3–20 (2010)
Xie, Y., Phoha, V.V.: Web user clustering from access log using belief function. In: Proceedings of the First International Conference on Knowledge Capture (K-CAP 2001), pp. 202–208. ACM Press (2001)
Shahabi, C., Banaei-Kashani, F.: A framework for efficient and anonymous web usage mining based on client-side tracking. In: Kohavi, R., et al. (eds.) WebKDD 2001. LNCS (LNAI), vol. 2356, pp. 113–144. Springer, Heidelberg (2002)
Ypma, A., Heskes, T.: Clustering web surfers with mixtures of hidden markov models. In: Proceedings of the 14th Belgian–Dutch Conference on AI (BNAIC 2002) (2002)
Nasraoui, O., Frigui, H., Joshi, A., Krishnapuram, R.: Mining Web Access Logs Using Relational Competitive Fuzzy Clustering. Presented at the Eight International Fuzzy Systems Association World Congress, IFSA 1999, Taipei (1999)
Tseng, F.C.: Mining frequent itemsets in large databases: The hierarchical partitioning approach. Expert Systems with Applications 40(5), 1654–1661 (2013)
Oyanagi, S., Kubota, K., Nakase, A.: Application of matrix clustering to web log analysis and access prediction. In: Third International Workshop EBKDD 2001—Mining Web Log Data Across All Customers Touch Points (2001)
Kivi, M., Azmi, R.: A webpage similarity measure for web sessions clustering using sequence alignment. In: Proceedings of 2011 International Symposium Artificial Intelligence and Signal Processing (AISP). IEEE Press (2011)
Bentley, J.: Multidimensional Binary Search Trees Used for Associative Searching. ACM 18(9), 509–517 (1975)
Bradley, P.S., Fayyad, U., Reina, C.: Scaling Clustering Algorithms to Large Databases. In: 4th International Conference on Knowledge Discovery and Data Mining (KDD 1998). AAAI Press (1998)
Scholkopf, B., Smola, J., Muller, R.: Technical Report: Nonlinear component analysis as a kernel eigen value problem. Neural Comput. 10(5), 1299–1319 (1998)
Dhillon, I.S., Fan, J., Guan, Y.: Efficient clustering of very large document collections. In: Data Mining for Scientific and Engineering Applications, pp. 357–381. Kluwer Academic Publishers (2001)
Elkan, C.: Using the Triangle Inequality to Accelerate k-Means. In: Proceedings of the Twentieth International Conference on Machine Learning (ICML 2003), pp. 609–616 (2003)
Yin, K.C., Hsieh, Y.L., Yang, D.L.: GLFMiner: Global and local frequent pattern mining with temporal intervals. In: 2010 the 5th IEEE Conference Industrial Electronics and Applications (ICIEA), pp. 2248–2253 (2010)
Baralis, E., Cerquitelli, T., Chiusano, S., Grand, A., Grimaudo, L.: An Efficient Itemset Mining Approach for Data Streams. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds.) KES 2011, Part II. LNCS, vol. 6882, pp. 515–523. Springer, Heidelberg (2011)
Zhao, C., Jia, B., Liu, Y., Chen, L.: Mining global frequent sub trees. In: 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), vol. 5, pp. 2275–2279 (2010)
Cai, C.H., Fu, A.W., Cheng, C.H., Kwong, W.W.: Mining association rules with weighted items. In: Proceedings of the International Database Engineering and Applications Symposium, IDEAS 1998, Cardiff, Wales, UK, pp. 68–77 (1998)
Tao, F.: Weighted association rule mining using weighted support and significant framework. In: Proceedings of the 9th ACM SIGKDD, Knowledge Discovery and Data Mining, pp. 661–666 (2003)
Wang, W., Yang, J., Yu, P.S.: WAR: weighted association rules for item intensities. Knowledge Information and Systems 6, 203–229 (2004)
Yun, U., Leggett, J.J.: WFIM: weighted frequent itemset mining with a weight range and a minimum weight. In: Proceedings of the 15th SIAM International Conference on Data Mining (SDM 2005), pp. 636–640 (2005)
Yun, U.: Efficient Mining of weighted interesting patterns with a strong weight and/or support affinity. Information Sciences 177, 3477–3499 (2007)
Xu, J., Liu, H.: Web User Clustering Analysis based on K-Means Algorithm. In: International Conference on Information Networking and Automation. IEEE (2010)
Liu, P., Li, W.: Navigation Pattern Discovery on Web Site Based on the Distance Between Sequences. Artificial Intelligence. In: Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC). IEEE Press (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Dixit, V.S., Bhatia, S.K., Kaur, S. (2014). Weighted-Frequent Itemset Refinement Methodology (W-FIRM) of Usage Clusters. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2014. ICCSA 2014. Lecture Notes in Computer Science, vol 8583. Springer, Cham. https://doi.org/10.1007/978-3-319-09156-3_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-09156-3_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09155-6
Online ISBN: 978-3-319-09156-3
eBook Packages: Computer ScienceComputer Science (R0)