COURSE UNIT TITLE

: TEXT MINING

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
CSE 5093 TEXT MINING ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR ADIL ALPKOÇAK

Offered to

Computer Engineering Non-Thesis
COMPUTER ENGINEERING
Computer Engineering Non-Thesis
Computer Engineering

Course Objective

This course aims to introduce the fundamentals of text mining, presents practical tools to be used in mining process and discusses different case studies based on text mining tools.

Learning Outcomes of the Course Unit

1   Define fundamentals related with Text Mining
2   Learn basic text processing tools and techniques
3   Comprehend tools and techniques to collect, analyze, retrieve, cluster, classify text data
4   Design and implement an Text Mining project for a given domain

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Overview of Text Mining
2 Handling Textual Data
3 Regular Expression
4 Reading & Writing Text Files
5 Modeling Text Data: Boolean Model
6 Modeling Text Data: Vector Space Model
7 Modeling Text Data: Statistical Modeling
8 Evaluation in Text Retrieval and Mining
9 Text Classification: k-NN
10 Text Classification: Naïve Bayesian
11 Text Classification: SVM
12 Text Clustering: k-means,
13 Text Clustering: Hierarchical Clustering
14 Text Clustering: EM Algorithm
15 Case Studies
16 Case Studies

Recomended or Required Reading

1. Sholom M. Weiss, Nitin Indurkhya, Tong Zhang, Fundamentals of Predictive Text Mining , Springer, 2010.
2. Rafael E. Banchs, Text Mining with MATLAB , Springer, 2013.

Materials: Recent research papers published various journals or proceedings, available at online library.

Planned Learning Activities and Teaching Methods

Course will be delivered as lecture in class. In addition, homework, programming assignment and small projects will be assigned.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 ASG ASSIGNMENT
2 PRS PRESENTATION
3 FCG FINAL COURSE GRADE ASG * 0.50 + PRS * 0.50


Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

Assoc.Prof.Dr.Adil ALPKOÇAK
Dokuz Eylul University, Dept of Computer Engineering
Tinaztepe, 35160 Izmir, Turkey
232-3017408

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 15 3 45
Preparations before/after weekly lectures 15 4 60
Preparing assignments 4 10 40
Preparing presentations 1 20 20
Reading 2 15 30
TOTAL WORKLOAD (hours) 195

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11
LO.153
LO.2553
LO.35553
LO.455535555

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2015 DEÜ. BİD

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