International Journal of Computer Applications (0975 – 8887) Volume 41– No.5‚ March 2012 Data Mining Application in Enrollment Management: A Case Study Surjeet Kumar Yadav Saurabh pal Research scholar‚ Shri Venkateshwara University‚ J. P. Nagar‚ (U.P.) India Head‚ Dept. of MCA VBS Purvanchal University‚ Jaunpur‚ India ABSTRACT In the last two decades‚ number of Higher Education Institutions (HEI) grows rapidly in India. This causes a cut throat competition among these institutions
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1. FOREWARD Authors as Adams‚ Khan‚ Hafiz and Raeside (1)‚ suggest some method for data collection‚ basing on the situation‚ warning from possible threats to the validity and reliability of data collected. Whatever the method of data collection chosen (observations‚ experimentation‚ survey‚ interviews‚ diary method‚ case study‚ data storage‚ triangulation)‚ there are several hypothesis that need to be considered since the beginning (1); the challenges born from the nature of the research and level
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Big data‚ Big rewards 1.Describe the kinds of big data collected by the organizations described in this case. There are mainly three kinds of big data collected by the organizations described in this case. First‚ IBM Bigsheets help the British Library to handle with huge quantities of data and extract the useful knowledge. Second‚ State and federal law enforcement agencies are analyzing big data to discover hidden patterns in criminal activity. The Real Time Crime Center data warehouse contains
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the Study 6. Motivation REVIEW OF RELATED LITERATURE AND STUDIES 1. Review of Related Literature 2. Review of Related Studies 3. Conceptual Framework 4. Operational Definition of Terms METHODOLOGY 1. Methods of Research 2. Data Gathering Techniques 3. Statistical Treatment of Data (optional) SYSTEM PRESENTATION A. Existing System 1. Company Background 2. Description of the System 3. Process Flow of the System 4. Analysis of the System B. Proposed System 1. Description of the System 2. Objectives of
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A Paper on Data preprocessing and Measures of Similarities and Dissimilarities and Data Mining Applications DEEPAK KUMAR D R M.SC IN COMPUTER SCIENCE 3RD SEMESTER‚ DAVANGERE UNIVERSITY deepakrdevang@gmail.com Abstract: This topic is mainly used by a number of data mining techniques‚ such as clustering‚ nearest neighbor classification‚ and anomaly detection. And it can also include the data mining applications.In this paper we have focused a variety of techniques‚ approaches and different areas
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The Data Protection Act (UK – 1998‚ Malta – 2001): The DPA concerns the “collection‚ recording‚ organization‚ storage‚ adaptation‚ alteration‚ retrieval‚ gathering‚ use‚ disclosure‚ blocking‚ erasure or destruction of personal data”. The purpose of the Data Protection Act: a. The purpose of the DPA is to protect living individuals against the misuse of their personal data. Examples of such misuse could include exposing of personal data without obtaining prior permission from the data subject
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structured data‚ accept queries from users‚ and respond to those queries. A typical DBMS has the following features (Stair and Reynolds‚ 2004): Provides a way to structure data as records‚ tables‚ or objects Accepts data input from operators and stores that data for later retrieval Provides query languages for searching‚ sorting‚ reporting‚ and other "decision support" activities that help users correlate and make sense of collected data Provides multi-user access to data‚ along with
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Generic Data Compression Techniques Data compression schemes fall into two categories. Some are lossless‚ others are lossy. Lossless schemes are those that do not lose information in the compression process. Lossy schemes are those that may lead to the loss of information. Lossy techniques provide more compression than lossless ones and are therefore popular in settings in which minor errors can be tolerated‚ as in the case of images and audio. In cases where the data being compressed consist
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1. An array is a list of data items that _____. are of different data types are all integers have different names are indexed | 2. An array that stores five days of closing stock prices can be declared as _____. decimal price1‚ price2‚ price3‚ price4‚ price5; decimal [] price = new decimal[5]; decimal price[] = new decimal[5]; decimal [] price = new price[5]; | 3. Which statement is true about this array declaration? int [] myArray = {1‚4‚3‚5‚6}; It declares a 5 dimensional array
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Calculating the Probability of a Type II Error To properly interpret the results of a test of hypothesis requires that you be able to judge the pvalue of the test. However‚ to do so also requires that you have an understanding of the relationship between Type I and Type II errors. Here‚ we describe how the probability of a Type II error is computed. A Type II error occurs when a false null hypothesis is not rejected. For example‚ if a rejection region
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