**Course Description and Introduction to Data Mining**

Overview of CS590D

*Tuesday, January 13, 1998*

**Database Perspectives on Data Mining**

The next few lectures will concentrate on the interplay between database systems and data mining. There are several issues that have been studied by researchers on how to to enhance database support for data mining. They can be broadly classified as:

- Enhancing the underlying model of data and DBMSs (The Logical Model of Data, Deductive Databases, Rules, Active Databases, Semistructured data, SDDF etc.)
- Enhancing the expressiveness of query languages (Rule Query Languages, Meta Queries, Query optimizations)
- Integration with Data Warehousing Systems (OLAP, Historical Data, Meta-Data, Interactive Exploring)

Of course, there are 3Cn combinations of these ideas, too.

**Database Perspectives on Data Mining (1)**

Introduction to Deductive Databases

*Thursday, January 15, 1998*

**Database Perspectives on Data Mining (2)**

Introduction to PROLOG and ILP systems

*Tuesday, January 20, 1998*

**Database Perspectives on Data Mining (3)**

Enhancing the expressiveness of query languages

*Thursday, January 22, 1998*

**Database Perspectives on Data Mining (4)**

Data Warehousing, OLAP etc.

*Tuesday, January 27, 1998*

**Database Perspectives (Misc. Themes)**

Associations, Rule Generation

*Thursday, January 29, 1998*

**Statistical Perspectives on Data Mining**

The next few classes will concentrate on the many different statistical viewpoints that have been offered for the data miner. In particular, we focus on issues in small and large sample size statistics, models and perspectives from statistical learning theory, as applied to data mining.

**Introduction**

Overview, Main Themes

*Tuesday, February 3, 1998*

**A Tutorial on Neural Networks**

More Statistical Perspectives

*Thursday, February 5, 1998*

**Perspectives from the AI community**

The distinction of lectures here is not very crisp here, but the following topics will form a smooth transition from the statistical viewpoints to ones offered by the AI researcher. This mostly involves what is known as "speedup" learning by the machine learning community.

**Classification**

Machine Learning Algorithms

*Tuesday, February 10, 1998*

**More ML algorithms**

Miscellaneous algorithms proposed by the AI community

*Thursday, February 12, 1998*

**Genetic Algorithms in Data Mining**

Introduction to Genetic Operators, Algorithms etc.

*Tuesday, February 17, 1998*

**Algorithmic Aspects (Time Series Analysis, Association Rules and Mining)**

**Time series analysis**

*Tuesday, February 24, 1998*

**Time series analysis (matching templates)**

*Thursday, February 26, 1998*

**Algorithms for Associations***Tuesday, March 3, 1998*

**More on Associations***Thursday, March 5, 1998*

**Algorithms and Strategies for Similarity Retrieval***Tuesday, March 17, 1998*

**Clustering In A High-Dimensional Space Using Hypergraph Models and**

Data Mining. Hypergraph Traversals, and Machine Learning*Thursday, March 19, 1998*

**Beyond Market Baskets: Generalizing Association Rules to Correlations**

Lecture Slides

*Thursday, March 26, 1998*

**Beyond Market Baskets: Generalizing Association Rules to Correlations (Contd.)**

Lecture Slides

*Tuesday, March 31, 1998*

**Mining the Web**

**Mining the WWW**

Introduction and Main Themes

*Thursday, April 2, 1998*

**Indexing by latent semantic analysis**

*Mon Apr 6 22:04:11 EST 1998*

**Parallelism in Data Mining**

**Parallel Formulations of Decision-Tree Classification Algorithms and ScalParC: A New Scalable and Efficient Parallel Classification Algorithm for Mining Large Datasets**

*Wed Apr 8 18:40:00 EST 1998*

**Parallel Data Mining for Association Rules on Shared-memory Multi-processors**

*Tue Apr 14 08:11:36 EST 1998*

**Towards a Cost-Effective Parallel Data Mining Approach**

*Thu Apr 16 11:17:54 EST 1998*

**Visualizing data mining results.**

*Mon Apr 20 22:47:50 EST 1998*