Bing Liu

Bing Liu

Associate Professor Department of Computer Science University of Illinois at Chicago (UIC) 851 S. Morgan (M/C 152) Chicago, IL 60607-7053

Tel: 1 (312) 355 1318 Fax: 1 (312) 413 0024

Past and present research interests: (after 1996-97) data mining, Web and text mining, machine learning, (before 1996-97) constraint satisfaction, route planning, and AI based scheduling.

Research Projects

• Learning from Positive and Unlabeled Examples
• Main feature of the problem: No labeled negative examples
• Download our LPU system - LPU and Spy-EM (S-EM) system - S-EM
• Web Content Mining, Text Mining, and Search
  • Opinion Extraction, Opinion Mining, and Feature-Based Summarization
    • We currently focus on consumer reviews on the Web. The system extracts product features that have been commented on by customers and determine their (positive and negative) opinons.
  • Web Data Extraction from Flat and Nested Data Records - automatic wrapper generation
    • Download ourMDR system; Two new systems, DEPTA and NET, but not for download yet.
  • Deep Search: Information Synthesis (or Knowledge Synthesis)
    • Given a search topic/query, discover sub-topics and related concepts and synthesize pieces of information from multiple pages to present a coherent and complete picture of the topic. Like generating a "book" on the topic.
  • Web page template detection and noise removal - page segmentation and cleaning
• Data Mining and Knowledge Discovery
  • Classification Based on Associations - Integrating Classification and Association Rule Mining
    • Download our system (CBA - Asociation rules based classification)
    • KDD-98 paper and others (KDD-99, PKDD-00, etc)
  • Association rule mining with multiple minimum supports
    • Very important for skewed data. Most real life data are skewed or highly imbalanced.
  • Interestingness: subjective interestingness, rule summary, rule query language, change mining
    • At Last, interestingness techniques have made it to the real world,. They play a central role in finding actionable knowledge in a deployed data mining system for Motorola. As a researcher on this topic for many years, this is paticularly satisfying.
  • Model Management: Managing and Analyzing Large Collections of Data Mining Models.

Teaching

• Spring 2006: CS 511 - Artificial Intelligence II
• Fall 2005: CS 583 - Data mining and text mining
• Past teaching

Professional Memberships

• Member, Association for Computing Machinery (ACM).
• Member, ACM Special Interest Group on Knowledge Discovery in Data and Data Mining (SIGKDD).
• Member, American Association of Artificial Intelligence (AAAI).
• Member, IEEE Computer Society.