A NEW APPORACH OF FREQUENT PATTERN MINING IN WEB USAGE MINING Mrs.R.Kousalya PhD. Scholar‚ Manonmaniam Sundaranar University‚ HOD/Asst professor‚ Dr. N.G.P. Arts and Science College‚ Coimbatore-641 048‚ India Mob no. +91 9894656526 Kousalyacbe@gmail.com Ms.S.Pradeepa M.Phil.Scholar‚ Department of Computer Science‚ Dr.N.G.P. Arts and Science College‚ Coimbatore-641 048‚ India Mob no. +91 9489551185 Prathy.it@gmail.com Ms.K.Suguna M.Phil.Scholar‚ Department of Computer Science‚ Dr.N.G.P. Arts and
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Case study: Jaeger uses data mining to reduce losses from crime and waste Leg of lamb is the most stolen item at Iceland. Thieves also like cheese‚ bacon and coffee. With the UK in recession‚ shoplifters appear to be switching their sights from alcohol‚ electric toothbrushes and perfume to food. Tesco‚ Marks & Spencer and Iceland have all reported an increase in shoplifting since the economy began to contract in the second quarter of 2008. Tesco alone caught some 43‚000 would-be thieves in the first
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Data Warehousing‚ Data Marts and Data Mining Data Marts A data mart is a subset of an organizational data store‚ usually oriented to a specific purpose or major data subject‚ that may be distributed to support business needs. Data marts are analytical data stores designed to focus on specific business functions for a specific community within an organization. Data marts are often derived from subsets of data in a data warehouse‚ though in the bottom-up data warehouse design methodology the data
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Data Preprocessing 3 Today’s real-world databases are highly susceptible to noisy‚ missing‚ and inconsistent data due to their typically huge size (often several gigabytes or more) and their likely origin from multiple‚ heterogenous sources. Low-quality data will lead to low-quality mining results. “How can the data be preprocessed in order to help improve the quality of the data and‚ consequently‚ of the mining results? How can the data be preprocessed so as to improve the efficiency and ease
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Topic 1: The Data Mining Process: Data mining is the process of analyzing data from different perceptions and summarizing it into useful evidence that can be used to increase revenue‚ cut costs or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles‚ categorize it and summarize the relationships identified. Association‚ Clustering‚ predictions and sequential patterns‚ decision trees and classification
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DATA INTEGRATION Data integration involves combining data residing in different sources and providing users with a unified view of these data. This process becomes significant in a variety of situations‚ which include both commercial (when two similar companies need to merge their databases and scientific (combining research results from different bioinformatics repositories‚ for example) domains. Data integration appears with increasing frequency as the volume and the need to share existing data explodes
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WORLD DATA CLUSTERING ADEWALE .O . MAKO DATA MINING INTRODUCTION: Data mining is the analysis step of knowledge discovery in databases or a field at the intersection of computer science and statistics. It is also the analysis of large observational datasets to find unsuspected relationships. This definition refers to observational data as opposed to experimental data. Data mining typically deals with data that has already been collected for some purpose or the other than the data mining
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Annotated Bibliography Data Mining Ayanso‚ A.‚ & Yoogalingam‚ R. (2010). Profiling Retail Web Site Functionalities and Conversion Rates: A Cluster Analysis. International Journal of Electronic Commerce‚ 14(1)‚ 79-113. doi:10.2753/JEC1086-4415140103 This article introduces the utilization of cluster analysis as a data mining tool. E-commerce has forced traditional businesses to reform their decision making processes and conduct its affairs based on activities occurring online. Monitoring
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Data warehousing is the process of collecting data in raw form for analyzing trends. The benefits to data warehousing are improved end-user access‚ increased data consistency‚ various kinds of reports can be made from the data collected‚ gather the data in a common place from separate sources and additional documentation of data. Potential lower computing costs‚ increased productivity‚ end-users can query the database without using overhead of the operational systems and creates an infrastructure
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Data Warehouse Concepts and Design Contents Data Warehouse Concepts and Design 1 Abstract 2 Abbreviations 2 Keywords 3 Introduction 3 Jarir Bookstore – Applying the Kimball Method 3 Summary from the available literature and Follow a Proven Methodology: Lifecycle Steps and Tracks 4 Issues and Process involved in Implementation of DW/BI system 5 Data Model Design 6 Star Schema Model 7 Fact Table 10 Dimension Table: 11 Design Feature: 12 Identifying the fields from facts/dimensions: MS: 12 Advanced
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