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<!DOCTYPE html>
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<h4><span class="alternate"style="font-weight: bold">Vasudha Bhatnagar</span></h4>
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<div class="section-title"><br><br><br><br>
<h3><span class="alternate"> MCS 203 (Jan - May 2019) Data Mining</span></h3>
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<div class="description-one">
<li type="none">Data has been accumulating throughout the computer age in many forms, including database systems, spreadsheets, text files, and recently web pages. Data mining aims to search through data for hidden relationships and patterns. It has application potential in both commercial and scientific domains. Data Mining has emerged as a field of inter-disciplinary studies in Computer Science. <br><br>
This is an introductory course on Data Mining. This is a 4 credit course (3-0-2), requiring intensive programming. Pre-requisites for this course are good understanding of statistics, C++ programming on Linux, and good practice of using and designing complex data structures.</li>
<br><br>
<h4>Text Book :</h4> <br>
<ol>
<li> Data Mining and Analysis: Fundamental Concepts and Algorithms : Zaki, Mohammed J., and Wagner Meira Jr., (Cambridge University Press, 2014) [Click <a href="http://www.cs.rpi.edu/~zaki/PaperDir/DMABOOK.pdf" style="text-decoration: none;color:cornflowerblue" target="_blank">here</a> to download draft copy of the book]</li>
</ol>
<br><br>
<h4>Reference Books :</h4><br>
<ol>
<li> Introduction to Data Mining: Tan, Stienbach and Vipin Kumar, (Pearson Education 2010)</li><br>
<li> Data Mining: Concepts and Techniques, Han and Kamber (Morgan Koffmann, 2010)</li><br>
<li> Principles of Data Mining, David J. Hand, Heikki Mannila and Padhraic Smyth(PHI)</li><br>
</ol>
<br>
<h4>Course Plan</h4> <br>
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<div class="subject">Topics from text book</div>
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<div class="speaker">1</div>
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<div class="subject">Chapter 1</div>
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<div class="speaker">2 - 7</div>
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<div class="subject">Clustering Algorithms: Hierarchical clustering (Chapter 14), Partition based - K means and Expectation-Maximization (Chapter 13), Kernal trick (Chap 5), Density based clustering -Dbscan (Chapter 15), Cluster validation</div>
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<div class="subject">Supervised Learning: Bayes Classification, KNN classifier (Chapter 18), Decision Tree (Chapter 19), Evaluation of classifiers (Chapter 22)</div>
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<div class="speaker">13 - 15</div>
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<div class="subject">Frequent item-set Mining (Chapter 8)</div>
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<h4>Lab Work</h4> <br>
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<div class="time">1 - 3</div>
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<div class="subject">Download and install Weka. Familiarize yourself with Weka. Explore data using Weka. Learn to use attribute filters for discretization, etc. Practice use of filters</div>   
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<div class="time">Click <a href="https://www.cs.waikato.ac.nz/ml/weka/downloading.html" style="text-decoration: none;color:cornflowerblue" target="_blank">here</a> for Weka</div>
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<div class="subject">Introduction to R, Visualization in R</div>   
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<div class="time">Click <a href="https://www.r-project.org/" style="text-decoration: none;color:cornflowerblue" target="_blank">here</a> to download R</div>
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<div class="subject">Start working on the project</div>   
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<div class="time">7 - 14</div>
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<div class="time">Submit report by 15 Apr 2019</div>
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<p>30 marks are for internal assessment.<br><br> Breakup is : <br> 10 marks - Minor 1, as per schedule displayed on the notice board. <br> 15 marks - Project <br>05 marks - Assignments</p> <br><br>
<h4>Project :</h4><br>
<p>You have to work in groups of four. Diversity in group will bring advantage.
<br>Visit open data portal of Government of India <a href="https://data.gov.in/" style="text-decoration: none;color:cornflowerblue" target="_blank">data.gov.in </a>. Choose a sector of your interest. Study the available data sets for the sector.</p><br>
<ol>
<li> Write a one page project proposal mentioning: Sector, Data sets intended to be used, Business questions that can be answered, Broad data mining techniques may be used to answer the questions. Submit the hard copy (single page) by 31st January 2019. </li><br>
<li> Explore datasets, write preliminary observations about the data quality, re-examine the business question and revise if required. Liberally use visualization techniques to prove your point. Ensure that the visualizations are meaningful and have appropriate legends, captions etc. Submit the soft copy of the report by 15th February 2019. No page limit.</li><br>
<li> Submit detailed report of the observed patterns in data. Clearly write the data mining technique used, algorithm used, reason for selecting the algorithm, pre-processing done. How do the observed patterns answer your business questions? Submit the soft copy of the report by 15th April 2019. No page limit.</li><br>
</ol>
<br>
<p>Syllabus for Minor 1: TBA</p>
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