1. Abstract (One page) One of the challenges for companies that have invested heavily in customer data collection is how to extract important information from their vast customer databases and product feature databases‚ in order to gain competitive advantage. The retail industry collects huge amounts of data on sales‚ customer buying history‚ goods transportation‚ consumption‚ and service. With increased availability and ease of use of modern computing technology and e-commerce‚ the availability
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Links: Online Analytical processing (OLAP) The best known knowledge discovery techniques are online analytical processing (OLAP) and data mining (DM) techniques (Turban et al.‚ 1999)
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Discussion Board 1 Research and describe 5 data collection techniques in your own words. Be sure to cite any sources you used in APA format. Answer the following questions: Why is the examination of collected data so important? How are statistics used in the field of criminal justice? There are so many ways to collect data that do not involve the common ways in a direct manner. We as individual people collect and store our memories in a few ways‚ and that is all data as well. Many people use their own
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Project Report Data mining for choosing the right place to advertise Hamida Idrissi Table of content 1. Introduction …………………………………………………………………….......2 2. Overview of data mining …………………………………………………………...2 2.1. What is data mining ………………………………………………………..3 2.2. The benefit of data mining and how information is obtained…………….3 3. Data mining and advertising ………………………………………………………4 3.1. How businesses choose the right place to advertise……………………….4 3.2. Data mining to choose the right
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Introduction Given a data-mining problem‚ you need to have data that represent the problem‚ models that are suitable for the data‚ and of course a data-mining environment that contains the algorithms capable of learning these models. In this lab you will study two well-known classification problems. You will try to find classification models for these problems using decision trees and decision rules. The algorithms to learn these models are given in Weka‚ a data-mining environment that accompanies
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An improved pre-processing technique with image mining approach for the medical image (Found in Endoscopy) classification. Rational and Significance The proposed system mainly concentrates on the diagnosis of Endoscopy Images . This work gives the Endoscopy Surgeons a second option for the easy identification of interior images of esophagus. The important data mining concept that has been included in the proposed work consists of pre-processing of the Endoscopy Images. The method used for
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What stocks are risky? What stocks in the portfolio that it has higher return? Many investors may use fundamental analysis to analysis financial data for answering above questions. In the last decade‚ some researches applied data mining techniques on financial market. Data mining is the process of automatically discovery useful information in large data repositories. It can be used to support a wide range of business intelligence applications such as customer profiling‚ targeted marketing‚ store
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amounts of data have been stored in computers. The existing database systems do not provide the users with the necessary tools and functionalities to capture all stored information easily. Therefore‚ automatic knowledge discovery techniques have been developed to capture and use the voluminous information hidden in large databases. Discovery of association rules is an important class of data mining‚ which is the process of extracting interesting and frequent patterns from the data. Association
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Description 2.1 Problem Definition 2.2 Problem Description 3 Data Mining Process And Implementation 3.1 Requirement analysis 3.2 Data Selection And Collection 3.3 Cleaning And Preparing Data 3.4 Data Mining Exploration And Validation 3.5 Implementing‚ Evaluating And Monitoring 3.6 Result visualization 4 Data Mining Model And Development Process 4.1 Algorithm Implementation 5 Data Mining Findings 6 Conclusion 7 Bibliography
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Above the line (ATL)‚ below the line (BTL)‚ and through the Line (TTL)‚ in organizational business and marketing communications‚ are advertising techniques. In a nutshell‚ while ATL promotions are tailored for a mass audience‚ BTL promotions are targeted at individuals according to their needs or preferences. While ATL promotions can establish brand identity‚ BTL can actually lead to a sale. ATL promotions are also difficult to measure well‚ while BTL promotions are highly measurable‚ giving marketers
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