<HTML> <HEAD> <TITLE>COSC 4335---a Data Mining Course</TITLE>
</HEAD>
<BODY BGCOLOR="E4FFFF" TEXT="#000000" LINK=darkred VLINK=brown ALINK=orange>
last updated: February 15, 2015<br>


<CENTER>
<br>
<H1><font color=red>COSC 4335: Data Mining in Spring 2015
(<a href="http://www.cs.uh.edu/~ceick/ceick.html">Dr. Eick </a>)</font></H1>
<br><br>
</CENTER>

<h2>Goals of the Data Mining Course</h2>
Data mining centers on finding valid, novel, interesting, and potentially useful patterns in data. 
It aims at transforming a large amount of data into a <em>well of knowledge</em>. Data mining
has become a very important field in industry as 
well as academia. For example, almost 900  papers were submitted 
for the IEEE International Conference
on Data Mining (ICDM) to be held in Shenzhen, China in December 2014
(<a href="DM_Conf_Rankings.docx">Data Mining 
Conference Rankings</a>). Data mining tools and 
suites (for example, see  <a href="http://www.kdnuggets.com/software/index.html">KDnuggets' DM Software 
Survey</a>) are used a lot in industry and 
in reseach projects.
<a href="http://www2.cs.uh.edu/~UH-DMML/index.html">UH's Data Mining and 
Machine Learning Group Website</a> (UH-DMML) conducts research in some of the 
areas that are covered by this 
course (<a href="http://www2.cs.uh.edu/~ceick/DM/2014.pptx">UH-DMML 
Research Overview</a>).  Finally, having 
basic knowledge in data mining is a plus when you are looking for a job in 
industry and at major US research 
institutions, such as the Texas Medical Center in Houston or at Federal Research Labs.<p>

The course covers the most important 
data mining techniques and provides background knowledge on how to conduct a 
data mining project. It also gives a basic introduction to data analysis. After defining 
what knowledge discovery and 
data mining is, data mining tasks such classfication, clustering, and association analysis will be discussed in 
detail. Basic data analysis techniques, centering on basic visualization techniques and statistics, 
to get a better understanding of the data mining task at hand will be covered.  
Moreover, techniques how to preprocess a data set for a data mining 
task will be introduced.  
Moreover, in 
course projects you will obtain hands on experience in conducting data mining and data analysis projects. Finally, 
as <a href="http://www.r-project.org/">R</a> will be 
used in most course projects; therefore, participants of the couse will obtain 
valuable exprience in using the R statistics, data mining, and visualization
packages and will learn how to write programs in R and how to develop data mining software 
on top to R. 
A recent 2013 poll Rexer Analytics found that R is currently the most 
popular data mining tool:  24% of the respondents use R as their primary 
tool, and only 30% of the respondents do not use R at all. Although R is a domain specific language, it's 
versatile.<p> In summary, having a sound background in data analytics and data mining and knowing R 
well  will open a lot of job opportunities for you, which, I believe, is a strong 
reason to take the course.


<H2>Comments concerning this website</H2>
If you have any comments
concerning this website, send e-mail 
to: <b><font color=red>ceick@uh.edu</font></b>
<h2>Basic Course Information</h2>
Instructor: <a href="http://www.cs.uh.edu/~ceick/ceick.html"> Dr. 
Christoph F. Eick </a><br>
office hours (573 PGH) TU 4-5p TH 11:30a-12:30p<br>
e-mail: ceick@uh.edu<br>
TA: Raju, Rezaul Karim

<br>
office hours: ...<br>
2015 TA website: <a href="https://sites.google.com/site/cosc4335datamining/
">Raju's 4335 
Website</a><br><br>
<a href="http://groups.google.com/forum/#!forum/cosc-6335-data-mining-fall-2014">Google COSC 
6335 News Group</a>
 <br>
class meets: TU/TH 2:30-4p<br>
cancelled classes: TBDL <br>
<a href="https://mynsmclouddrive.uh.edu/public.php?service=files&t=a505a38e9ceb7fb27d07df0304ac8280">COSC 4335 Lecture Video Link</a>

<H2>Course Materials</H2>
<a href="COSC4335-Syllabus.docx">COSC 4335 Syllabus</a><br>
<a href="Data_Mining_2012.pptx">Objectives Data Mining Course</a>
<br><br>
<DL><B>Recommended Text:</B> 
<DD>P.-N. Tang, M. Steinback, and V. Kumar: <I>Introduction to Data Mining</I>,
  <DD>Addison Wesley, 2006,
  <DD><A href="http://www-users.cs.umn.edu/~kumar/dmbook/">Link to 
  Book HomePage</A> <BR><BR></DD></DL>
<DL><B>Other Material:</B> 
    <DD>Jiawei Han and Micheline Kamber, <I>Data Mining: Concepts and 
  Techniques</I> 
  <DD>Morgan Kaufman Publishers, Third Edition, 2011.
  <DD><A href="http://www.cs.illinois.edu/~hanj/bk3/">Link to Data Mining Book Home 
  Page</A><br><br>
<a href="http://www.itl.nist.gov/div898/handbook/">NIST/SEMATECH e-Handbook of Statistical Methods</a> (good onlne source covering  
exploratory data analysis, statistics, modelling and prediction)
</DD></DL>


<h2>Important Dates in 2015</h2>
Thursday, February 26: Nguyen Pham will be teaching this lecture <br>
Tuesday, March 10: Midterm exam <br>
Tuesday, March 24: N.N. will be teaching this lecture <br>
Tuesday, April 7: Maybe, R programming (may be takehome) exam <br>
Tuesday, April 21: Project3 Student Presentations<br>
TU/TH, May ?, 2p: Final Exam (will not be comprehensive) 



<h2>2015 Assignments </h2>
<a href="Project1-15.doc">Draft of Assignment1</a><br>

Assignment2: <a href="Project2-15.docx">Traditional Clustering with K-means and DBSCAN</a>; (<b>Individual Project</b>,   preliminary draft; you are expected to start working on the 
project after the lecture on February 24;  <a href="Randomized Hill Climbing.pptx">Randomized Hill Climbing Slides</a>)<p>



<h2>News COSC 4335 (Data Mining) Spring 2015</h2>
<UL>
<LI> The specification of Assignment1 problem 6 has been updated. 
<LI> This website is an evolving document. As Dr. Eick teaches this course the first time, more teaching material will be added to this website as we move along in the semester. 
<LI> A first draft Assignment2 (including the <i>2015 Abalone Data Mining Cup</i>) has been posted; please, read its specification before the lecture on Feb. 24 which will discuss its tasks. 
<LI> The lectures in the Feb. 16 week will center on clustering, and there will an R-lab on Th., Feb. 26 (bring labtops) to prepare you for the tasks of Assignment2. 
</UL>

<h2>Prerequisites</h2>

COSC 3380  and MATH 3336. 
<H2>Course Elements and Their Tentative Weights for 2014</h2>
Assignments (4): 41-50%<br>
Exams (2-3): 48-55% <br>
Class Attendance: 1% <br>
Special Individual Tasks: 0-5%
<h2>2015 Projects</h2>
<h2>COSC 4335: Data Mining Lecture Notes</h2>
I Introduction to Data Mining (<a href="Cosc4335-Knowledge_Sources.docx">COSC 4355 Knowledge Sources</a>, <a href="DM-Part1a.pptx">Part1</a>, <a href="DM-Part1b.pptx">Part2</a>, 
<a href="DM-Part1c.pptx">Part3: Data</a>, 
<a href="claclu.pptx">Differences
between Clustering and Classification</a> --- covers chapter 1 and Section 2.1)<br>
II <a href="DM-Part2.pptx">Exploratory Data Analysis</a> (covers chapter 3 in part; see also <a href="dm_interpreting.ppt">Interpreting Displays</a>)<br>

III R (<a href="Arko_R_Sh_Intro.pptx">Arko's Short Intro Into R</a>, <a href="UDM-R1.pptx">Data and Some R Data Analysis  Functions</a> (download datasets prior to the Feb. 5 lecture!),
<a href="http://stats.stackexchange.com/questions/30788/whats-a-good-way-to-use-r-to-make-a-scatterplot-that-separates-the-data-by-trea">Scatter Plot Code</a>, <a href="DecisionTreesinR.pptx">, Decision Trees in R</a>, <a href="bna.r">Some useful code for Project1</a>)<br>

IV Clustering and Similarity Assessment (<a href="dm_clustering1.pptx">Introduction</a> and <a href="dm_clustering2b.pptx">Hierachical Clustering and DBSCAN</a>;
R-scripts demonstrating: <a href="r-clustering.r">K-means/medoids</a>, <a href="dbscan.r">DBSCAN</a>;  
<a href="Clustering_Exercises1.pdf">Clustering Exercises
K-Means, HC, and DBSCAN</a>) 

</body></html>