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. 数据挖掘研究院
Research Interests |
Publications |
Professional Activities |
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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
- Opinion Extraction, Opinion Mining, and Feature-Based Summarization
- 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.
- Classification Based on Associations - Integrating Classification and Association Rule Mining
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.
First Draft: by Bing Liu on April 10, 2002.

