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50120130406017

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  • 1. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME TECHNOLOGY (IJCET) ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 4, Issue 6, November - December (2013), pp. 156-160 © IAEME: www.iaeme.com/ijcet.asp Journal Impact Factor (2013): 6.1302 (Calculated by GISI) www.jifactor.com IJCET ©IAEME EFFECTIVE WEB MINING TECHNIQUE FOR RETRIEVAL INFORMATION ON THE WORLD WIDE WEB Miss Purvi Dubey Information Technology, Medicapas Institute of Science and Technology, RGPV Indore (M.P.), India Asst. Prof. Sourabh Dave Information Technology, Medicapas Institute of Science and Technology, RGPV Indore (M.P.), India ABSTRACT In today’s age the major problem is related to the predicting user’s web page request. In the past few years the markov model is used for this problem. The effective web mining techniques like Clustering, Association rule mining and markov model having many drawbacks. We are proposing an approach for overcome that drawbacks which help us to improving search engine and helping user to find interesting web pages. Keywords – Association rule mining, Clustering, Hybrid model, Markov model, Page Rank algorithm. 1. INTRODUCTION Web is a complicated system of interconnected elements, the method of gaining coal or other minerals from a mine is called mining, so the web mining is an application of data mining techniques to explore pattern from the web [1]. 1.1 Classification Of web Mining –web mining can be classified in to the three subheadings 1.1.1 Web content mining: - Web content mining is the process of extracting information from content of document, so it is called web content mining. 156
  • 2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME 1.1.2 Web structure mining: - Web structure mining is the process of getting a conclusion from reference book in the web. 1.1.3 Web Usage mining: - web usage mining is the process of extracting patterns in web access log and it is also called web log mining [2]. 2. WEB MINING TECHNIQUES Web mining techniques having the following points:2.1 PageRank Pagerank works by counting the number and quality of links to a page for determine a rough estimate how important a web site is. Pagerank is defining as follows: We assume page a has pages T1..., Tn, which point to it (i.e., are citations). The parameter d is damping factor which can be set between o and 1 and is usually set 0.85. Out_deg (A) denotes the number of links going out of page A (out-degree of A). Definition 2.1.1 PageRank: - The page rank of a page A is given as follows: ௉ோሺ்௜ሻ ܴܲ ሺ‫ܣ‬ሻ ൌ ሺ1 െ ݀ሻ ൅ ݀ ቂ∑௡ ௉ோሺ்௜ሻቃ (1) ௜ୀଵ Page rank of PR(A) cab be calculated using a simple iterative algorithm, and corresponds to the principal eigenvector of the normalized link matrix of the web. Let n be the number of documents we have. We define the link matrix M, where the Mij entry is 1/nj if there is a link form document j to document i, otherwise Mij is o. nj is the number of the forwarded link of document. Then we can compute the PageRank on the graph which is the dominant eigenvector of the matrix A [5]. 2.2 Clustering The clustering is a effective process in which search result combine it to a interesting page groups according to the typed query and allows user to navigate into a document quickly. 2.3 Markov Model A traditional markov model describing a sequence of possible event in which the probability of each event depends only on the state attained in the previous event. 2.4 Association rule mining In data mining association rule mining is a popular and well researched method for discovering interesting relations between variables in large database. It is related to identify strong rules discovered in database using measure of interestingness. 3. PROBLEM FORMULATION T The web mining techniques having the following problems. 3.1 PageRank The PageRank work well when the search results are good, user can easily go to the interested page , from top ranked result which is found by the PageRank. The problem arises when the search results are several types. To overcome this problem clustering is used. To overcome this problem clustering is used. 157
  • 3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME 3.2 Clustering As we discussed earlier clustering is defined as the process in which search results combine into a interesting page group according to the typed query and allows user to enter a document quickly. The general idea of clustering is to partition the search results into clusters and the search result is a set of top-rank research result is called clustering, the clustering also having some drawbacks that are:(i) (ii) The organizing way of clustering is not always deliver a correct information as expected by user, for example if a user want to find the “phone number”or” mobile number”when entering the query “contact number” , but the cluster discovered by the current method may partition the results into “flat number ” and “street number” such cluster would not be very useful for users. Sometime, the cluster captions are not describable so it misleads the user to find search results. To overcome from this problems many strategies are used in which the problem solved in the part. And the method fails if we want the results of single query in the query clusters. Other methods analyzed the different clicked url of a query in user click through logs directly. The number of different clicked is not related to the type of query which user wants. 3.3 Markov model One limitation of applying the markov model techniques to the web personalization and prediction is the difficulty of data interpretation and visualization. The main problem that faces the markov model users is the identification of an optimal number of markov model orders. 3.4 Limitations of association rule mining The main problem with association rule mining is the frequent item problem where the items that occur together with a high frequency will also appear together in many of the resulting rules, and thus, resulting in inconsistent predictions. 4. PROPOSED ALGORITHM As we discussed in problem formulation part the PageRank and clustering having some drawbacks, our main aim is improving the search engine delivery results, so we are proposing the following approach (i) (ii) (iii) (iv) (v) (vi) (vii) (viii) (ix) (x) Pick access log information from the web server. From the log information retrieve only IP address and URL form web server and prevent noise and filtrate the inconsequential expansion. Keep each transaction in individual cluster and create click stream transaction. Now, explore similarity between transactions. Recognize first K neighbors having similarity more than threshold, for each transaction and remove other neighbors. Group the pairs with highest similarity. Update equality for objects in the neighborhood of combined pair. Detect new set of K neighbors from 2K neighbor of combined pair. Update the neighbor in the previous list of combined pairs. Reduplication of step (vi) to (vii) until no more combination is possible. 158
  • 4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME (xi) (xii) (xiii) (xiv) (xv) (xvi) Detect the clusters, the one having access sequence equal to test sessions. Begin with highest possible value of K. Apply the markov-model and explore K-th order states for the session from its cluster. If the support is so much less, estimate next lower order states for the test session from its cluster. Again apply step (xiv) until states are generated with enough support. Show the page with highest probability as the recommended page. 5. RESULTS In this section we will present the experimental results to check the performance of proposing algorithm. Fig 1 describes better web page prediction accuracy by applying proposed algorithm as compare to applying the traditional markov model. The accuracy of prediction is given by: Acc=Te ∩ Tr / Te (2) Where, Te is the number of test case and Tr is the number of training cases. Figure shows that the accuracy of proposed algorithm is much higher than the traditional markov model. 100 90 80 70 60 50 40 30 20 10 0 Traditional Markov Model Proposed Algorithm Figure: - Accuracy of proposed algorithm compare to traditional markov model We have applied proposed model on 60 test sessions and we found 51 session having accurate predictions while the traditional model gave only 42 sessions accurate prediction out of 60 test sessions, the proposed model having 85% accuracy while the traditional model have 70% accuracy. 159
  • 5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME 6. CONCLUSION The proposed algorithm will help users to find interesting web pages. It also enhances the search result delivery. The limitations of the proposed approach algorithm it is based on estimate so the result is not always exact. The result shown in the figure shows the accuracy of proposed algorithm as compare to traditional markov model. The Proposed approach can be used to enhance the search engine results and main advantage of this algorithm it is used to promote e-commerce, online marketing, purchasing of goods and services. REFERENCES [1] Cooley, B. mobasher, and J. Shrivastava, Web mining: Information and pattern discovery on www, IEEE, 1997, 1082-3409. [2] Pooja Sharma, Asst.prof Rupali bhartiya, An efficient Algorithm for improved web usage mining, ISSN, 3(2), 766-79. [3] Miss shital c.patil , prof. R.R keole, Web usage mining and web content mining- A combine approach for enhancing search engine delivery, ISSN, 3(10),2013,800-803. [4] Poonam kaushal, Hybrid model for better prediction of web page, International journal of scientific and research publication,2(8),2012,1-4. [5] Arun K Pujari, Data mining techniques (Hyderabad (a.p.) India universities press (India) Private limited, 2010) [6] Poonam Kaushal, Prediction of user’s next web page request by hybrid technique, International Journal of engineering technology and advanced engineering, 2(3),2012, 339-342. [7] Suresh Subramanian and Dr. Sivaprakasam, “Genetic Algorithm with a Ranking Based Objective Function and Inverse Index Representation for Web Data Mining”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 5, 2013, pp. 84 - 90, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [8] M. Karthikeyan, M. Suriya Kumar and Dr. S. Karthikeyan, “A Literature Review on the Data Mining and Information Security”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 1, 2012, pp. 141 - 146, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [9] Alamelu Mangai J, Santhosh Kumar V and Sugumaran V, “Recent Research in Web Page Classification – A Review”, International Journal of Computer Engineering & Technology (IJCET), Volume 1, Issue 1, 2010, pp. 112 - 122, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [10] Prakasha S, Shashidhar Hr and Dr. G T Raju, “A Survey on Various Architectures, Models and Methodologies for Information Retrieval”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 1, 2013, pp. 182 - 194, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [11] R. Lakshman Naik, D. Ramesh and B. Manjula, “Instances Selection Using Advance Data Mining Techniques”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 2, 2012, pp. 47 - 53, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [12] Prof. Sindhu P Menon and Dr. Nagaratna P Hegde, “Research on Classification Algorithms and its Impact on Web Mining”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 4, 2013, pp. 495 - 504, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. 160

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