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For accessing the information from web log, this is very important task and this task can be accomplished by web usage mining technique. Through web usage mining technique we can find out visitors behavior which can automatically and very fast access intrinsic information from huge amount of web log data, such as interesting access path, identify the user, accessing the web page group, web user clustering and web pre-fetching. Web usage mining is milestone for decision making process for an organization. Data preprocessing is very important concepts for the mining process. If our web log data is preprocessed then we can easily find out the desire information about visitor and also retrieve other hidden information from web log data. In this paper we focus on data preprocessing technique of web usage mining, after completion of data preprocessing, any king of irrelevant information can be sort out. We have also proposed an algorithm and its implementation for web log preprocessing in web usage mining. Every page has been assigned with an individual token. According to this token and frequency, data mining technique (Classification, Association Rules, and Clustering) can be applied. In this article we can easily find the highest and lowest value according to page access frequency.

Web Usage Mining, Preprocessing, Web Log Data, Frequency, Clustering

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[5] Shaimaa Ezzat Salama, Mohamed I. Marie, "Web Server Logs preprocessing for Web Intrusion Detection," Computer and Information Science, vol. 4, 2011.
[6] Jaideep Srivastava, Robert Cooley, Mukund Deshpande and Pang-Ning Tan, "Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data," SIGKDD Explorations, vol. 1, 2000.
[7] Liu Kewen, "Analysis of Preprocessing Methods for Web Usage Data," International Conference on Measurement , Information and control(MIC),IEEE,2012.
[8] R. Cooley,B. Mobasher and J Shrivastava, "Web Mining:information and pattern discoveryon the World Wide web," Ninth International Conference, 2011.
[9] Web Log Data, "http://ita.ee.lbl.gov/html/contrib/NASA-HTTP.html,".
[10] Zhuang Like, Kou Zhongbao and Zhang Changshui, "Session identification based on time intervals in Web log mining," Journal of Tsinghua University (Science and Technology), 2005.
[11] N. Zhang and W. F. Lu, " An Efficient Data Preprocessing Method for Mining Customer Survey Data," IEEE, 2007.
[12] Tasawar Hussain, Dr. Sohail Asghar, Dr. Nayyer Masood, " Web Usage Mining: A Survey on Preprocessing of Web Log File," IEEE, 2010.
[13] T. Murata and K. Saito, "Extracting Users` Interests from Web Log Data," Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings, 2006.
[14] Ling Zheng , Hui Gui and Feng Li, "Optimized Data Preprocessing Technology for Web Log Mining," International Conference On Computer Design And Appliations ICCDA, 2010.
[15] R. Cooley, B. Mobasher and J. Srivastava, "Data preparation for mining world wide web browsing patterns," Knowledge and Information System, 1999.
[16] Brijesh Bakariya and G.S.Thakur, "Preprocessing on Web Log Data in Web Usage Mining," International Conference on Intelligent Computing and Information System ICICIS, 2012.
[17] Thi Thanh Sang Nguyen, Hai Yan Lu and Jie Lu, "Web-page Recommendation based on Web Usage and Domain Knowledge," IEEE, 2013.

B. Bakariya, G.S. Thakur, "Effectuation of Web Log Preprocessing and Page Access Frequency using Web Usage Mining," International Journal of Computer Sciences and Engineering, Vol.1, Issue.1, pp.1-5, 2013.

At present time huge amount of useful data is available on web for access, and this huge amount of data is shared information which can be used by anyone intended to use. The availability of different types and nature of document data has lead to the task of clustering in large dataset. Clustering is one of the very important techniques used for classification of large dataset and widely applicable many areas. High-quality and fast document clustering algorithms play a significant role to successfully navigate, summarize and organize the information. Recent studies have shown that partitional clustering algorithms are suit- able for large datasets. The k-means algorithm [9, 10] is generally used as partitional clustering algorithm because it can be easily implemented and is most efficient in terms of execution time. The major problem with this algorithm is its sensitivity in selection of the initial partition and its convergence to local optima. In this research study we have refined the useful information from document data set using minimum spanning tree for document clustering and good quality of clusters have been generated on several document datasets, and the output show obtained indicates effective improvement in performance.

Minimum Spanning Tree, Document Clustering, World Wide Web, K-Means Algorithm

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D.S. Rajput, R.S. Thakur, G.S. Thakur, "Clustering approach based on Efficient Coverage with Minimum Weight for Document Data," International Journal of Computer Sciences and Engineering, Vol.1, Issue.1, pp.6-13, 2013.

This paper presents the design of a broadband Low Noise Amplifier (LNA). The work presented by the structure is capable of operating on a frequency band stretching from 0.8GHz to 2.5GHz i.e. covering most of the high speed data applications. Common source LNA design is offered in this paper. The design is implemented using 0.18�m CMOS process with a supply voltage of 1.3V. The Cascade LNA achieves 12.8 dB (min.) gain, 0.44 dB (min.) NF over operating frequency spectrum upholding high degree of stability factor. LNA presented in this paper consumes 19mW of power.

Broadband, LNA, Common Source, RF

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M.B. Thacker, S.S. Bhoyar, P.K. Rahangdale, "Broadband CMOS LNA Design and Performance Evaluation," International Journal of Computer Sciences and Engineering, Vol.1, Issue.1, pp.14-19, 2013.

The IT revolution is one of the biggest achievements of mankind. The use of information technology is very much prominent in all organizations irrespective of their size. Banking sector is one of those sectors which has incorporated the use of IT in all kinds of operations being done by them. The size of data bases in banks is increasing at such an alarming rate that banks are not able to manage the data bases and hence are unable to use the data for proper decision making. Some of the leading banks around the globe have started using the latest techniques of data mining for customer segmentation, while some banks are still at the planning stage for the implementation of the same. The primary objective of this paper is to investigate the problems faced by the banks while handling the data and to check whether the banks which are using data mining technology are better in this regard as compared to the banks which are not using data mining techniques.

Data Mining, Decision Making, Fraud Detection, Customer Relationship Management

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V. Bhambri, "Data Mining as a Solution for Data Management in Banking Sector," International Journal of Computer Sciences and Engineering, Vol.1, Issue.1, pp.20-25, 2013.

Document set is the collection of different types of document. Each document contains special type of information, which is beneficial for the peoples. We have the need of document clustering by their similarity. Document may contain data related to the blogs, website access pattern, any transaction or simply text. By the clustering of similar documents one can find the future trends of the people and it is also useful for the business point of view. In this paper, we have proposed a clustering approach for large size document sets. This proposed approach immediately assign document into appropriate cluster. Experiments are conducted with the twenty newsgroup dataset using java and MATLAB software. Comparisons are also performed with the existing methods. Experimental results show the effectiveness of the proposed approach for large document sets.

Large Document Set, Similarity measurement, Term Extraction, Dendrogram

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K.K. Mohbey, G.S. Thakur , "A Clustering Framework for Large Document Datasets," International Journal of Computer Sciences and Engineering, Vol.1, Issue.1, pp.26-30, 2013.