IEEE Computer Society

Miami Chapter

Seminar Presentation

Placation: Ungar 402, University of Miami, Coral Gables

Date and Time: February 21st 2006, 5:00pm

Topic: Data Mining for Exploration of Databases
By: Dr. Mitsunori Ogihara, University of Rochester

Data mining is a new area of computer science that integrates ideas from other areas of computer science, such as algorithms, complexity theory, computer systems, databases, machine learning, and network systems. The goal of data mining is to discover interesting information in large databases. In this talk I will present some of my past and current research work in the area of data mining. I will first introduce association mining and sequence mining, both of which have been known to be quite useful in businesses. In the former the goal is to enumerate all frequent combinations of data attributes appearing in a database, in the latter the data is time- stamped and the goal is to find all frequent sequences of frequent combinations of data attributes. I will present how these data mining tasks can be used for slightly different purposes, for finding similarities among database and for predicting future events. I will then switch a gear and talk about some work on gene expression data analysis. Semi-supervised learning refers to machine learning techniques for building more accurate models creatively using unlabeled data. I will present how semi- supervised learning can be used to improve the accuracy of gene function classification. I will then speak about multi-class sample classification using gene expression data.