CS 4445 B Term 2014 - Lecture Notes

Computer Science Department

CS 4445 Data Mining and Knowledge Discovery in Databases
LECTURE NOTES - B Term 2014

PROF. CAROLINA RUIZ

WARNING: These lecture notes are intended as supporting material not as substitute for attending the lectures.

Some sample datasets (including the ones provided with the Weka System)

Lecture: Combining Multiple Models

Lecture: Web Mining

Useful Slides and Class Handout:
- "Web Mining - Accomplishments & Future Directions." by Jaideep Srivastava, Prasanna Desikan, and.Vipin Kumar.
- Jaideep Srivastava's Web Mining Tutorial.
- Section 9.6 and 17.5 of the Weka Book (3rd edition)

Lecture: Text Mining

Useful Slides and Class Handout:
- Information Retrival and Text Mining
  - Christopher D. Manning, Prabhakar Raghavan & Hinrich Schütze. "Introduction to Information Retrieval". Cambridge University Press. 2008.
- Singular Value Decomposition (SVD), Principal Components Analysis (PCA)
  - M.W. Berry, Z. Drmac, and E.R. Jessup, "Matrices, Vector Spaces, and Information Retrieval", SIAM Review, Vol.41, No.2, pp.335-362, 1999.
- N. Evangelopoulos and L. Visinescu. "Text-Mining the Voice of the People". Communications of the ACM. Vol. 55, No. 2. pp. 62-69. February 2012.

Lecture: Data, Data Preprocessing, Measures of Similarity

Useful Slides and Class Handout:
- Tan's, Steinbach's, and Kumar's textbook slides - Chapter 2.
  Data Preprocessing: slide 26
  Missing Values: slide 24
  Attribute Discretization: slides 44-45
  Sampling: slides 29-32
  Dimensionality reduction, Feature Selection: slides 40-41
  Dimensionality reduction, PCA: slides 36-37, 39
  Feature Creation: slide 42
  Correlation: slides 62-63
- Witten's and Frank's textbook slides - Chapter 2.
- Witten's and Frank's textbook slides - Chapter 3.

Lecture: Introduction to the Weka System

Useful Slides and Class Handout:
- Witten's and Frank's textbook slides - Chapter 2.
Lecture Objectives:

The main objective of this lecture is to become familiar with the Weka system.
1. WekaTextbook slides provided above.
2. Work with the Weka System
  1. Get familiar with the sample datasets provided with the Weka system.
  2. Open the contact lenses dataset contact-lenses.arff with a word editor. The file consists of:
    - An OPTIONAL header with general information about the dataset. Each line in this header starts with "%" to denote that these are commented lines.
    - The name of the dataset
```
  @relation contact-lenses
                           
```
    - The name and types of the attributes in the dataset
```
  @attribute age                  {young, pre-presbyopic, presbyopic}
  @attribute spectacle-prescrip   {myope, hypermetrope}
  @attribute astigmatism          {no, yes}
  @attribute tear-prod-rate       {reduced, normal}
  @attribute contact-lenses       {soft, hard, none}
                           
```
      These attributes are the "column names" of the tabular data that comes after.
    - The dataset instances
```
  @data

  young,myope,no,reduced,none
  young,myope,no,normal,soft
  young,myope,yes,reduced,none
  young,myope,yes,normal,hard
   ...

                   
```
3. Run the Weka system. Choose the "Explorer" option in the main window.
4. Load the contact lenses dataset into the Weka System using "Open File".
5. Apply the following classification techniques (under "Classify") to this dataset
  - ZeroR: 0 predicting attributes are used to construct a classification rule
  - OneR: 1 predicting attribute is used to construct classification rules
  - J48: Decision Trees
6. If time allows, apply the following association technique (under "Associate") to this dataset
  - Apriori
7. Go back to the "Preprocess" tab and modify the input dataset using the following preprocessing filters. Access the filters by clicking on the "Choose" button.
  - Remove This filter removes one or more attributes from the current dataset.
    1. Select "Remove" from "weka->filters->unsupervised->attribute"
    2. Click on "Remove" to access the Weka window with the parameters for this filter
    3. Click on "More" to learn more about this filter
    4. Use say "1-2" in the "AttributeIndices"
    5. Click "OK"
    6. Click "Apply" on the main Weka Window
    7. Describe how the dataset changed
  - Resample This filter selects a random sample from the data instances in the current dataset.
    1. Select "Resample" from "weka->filters->unsupervised->instance"
    2. Click on "Resample" to access the Weka window with the parameters for this filter
    3. Click on "More" to learn more about this filter
    4. Use say "20.0" in the "SampleSizePercent" to select a subsample of 20% of the current instances.
    5. Click "OK"
    6. Click "Apply" on the main Weka Window
    7. Describe how the dataset changed
8. If time permits, discuss how to convert a dataset to ARFF (attribute relation file format). Use for example the Titanic Dataset. Look at the dataset description and the Data instances.

Lecture: Introduction to KDD and Data Mining

Useful Slides and Class Handout:
Lecture Objectives:
1. To learn in detail the different steps of the Knowledge Discovery in Databases (KDD) process.
2. To learn more about KDD and data mining applications.
3. To learn about some commercial and academic data mining systems.
4. To learn about additional data mining and KDD resources (books, journals, conferences).
5. Course Logistics
  - See the course webpage containing the course syllabus
  - See the course schedule of topics.
Additional Notes:
- Before our next lecture everybody should:
  - Read Chapter 1 of the textbook
  - Download the Weka system according to the syllabus instructions
  - Play with the Weka system in preparation for next lecture
  - Read the Weka tutorial that comes with Weka to become familiar with the Weka system.

CS 4445 Data Mining and Knowledge Discovery in Databases LECTURE NOTES - B Term 2014

PROF. CAROLINA RUIZ

Some sample datasets (including the ones provided with the Weka System)

Lecture: Combining Multiple Models

Lecture: Web Mining

Lecture: Text Mining

Lecture: Anomaly Detection

Lecture: Clustering

Lecture: Association Rules

Lecture: Rule Based Classifiers

Lecture: Bayesian Networks

Lecture: Model Evaluation

Lecture: Decision Trees

Lecture: Data Exploration, Visualization, OLAP

Lecture: Data, Data Preprocessing, Measures of Similarity

Lecture: Introduction to the Weka System

Lecture: Introduction to KDD and Data Mining

CS 4445 Data Mining and Knowledge Discovery in Databases
LECTURE NOTES - B Term 2014