Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness. In any given transaction with a variety of items, association rules are meant to discover the rules that determine how or why certain items are connected.
Apriori is an algorithm for frequent item set mining and association rule learning over relational databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database. The frequent item sets determined by Apriori can be used to determine association rules which highlight general trends in the database: this has applications in domains such as market basket analysis.
Héctor García-Molina was a Mexican-American computer scientist and Professor in the Departments of Computer Science and Electrical Engineering at Stanford University. He was the advisor to Google co-founder Sergey Brin from 1993 to 1997 when Brin was a computer science student at Stanford.
SIGKDD, representing the Association for Computing Machinery's (ACM) Special Interest Group (SIG) on Knowledge Discovery and Data Mining, hosts an influential annual conference.
Jiawei Han is a Chinese-American computer scientist and writer. He currently holds the position of Michael Aiken Chair Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. His research focuses on data mining, text mining, database systems, information networks, data mining from spatiotemporal data, Web data, and social/information network data.
Tomasz Imieliński is a Polish-American computer scientist, most known in the areas of data mining, mobile computing, data extraction, and search engine technology. He is currently a professor of computer science at Rutgers University in New Jersey, United States.
Hans-Peter Kriegel is a German computer scientist and professor at the Ludwig Maximilian University of Munich and leading the Database Systems Group in the Department of Computer Science. He was previously professor at the University of Würzburg and the University of Bremen after habilitation at the Technical University of Dortmund and doctorate from Karlsruhe Institute of Technology.
Foster Provost is an American computer scientist, information systems researcher, and Professor of Data Science and Information Systems and Ira Rennert Professor of Entrepreneurship at New York University's Stern School of Business. He is also the Director for the Data Science and AI Initiative at Stern's Fubon Center for Technology, Business and Innovation. Professor Provost has a Bachelor of Science from Duquesne University in physics and mathematics and a Master of Science and Ph.D. in computer science from the University of Pittsburgh.
Rakesh Agrawal is a computer scientist who until recently was a Technical Fellow at the Microsoft Search Labs. Rakesh is well known for developing fundamental data mining concepts and technologies and pioneering key concepts in data privacy, including Hippocratic Database, Sovereign Information Sharing, and Privacy-Preserving Data Mining. IBM's commercial data mining product, Intelligent Miner, grew out of his work. His research has been incorporated into other IBM products, including DB2 Mining Extender, DB2 OLAP Server and WebSphere Commerce Server, and has influenced several other commercial and academic products, prototypes and applications. His other technical contributions include Polyglot object-oriented type system, Alert active database system, Ode, Alpha, Nest distributed system, transaction management, and database machines.
Usama M. Fayyad is an American-Jordanian data scientist and co-founder of KDD conferences and ACM SIGKDD association for Knowledge Discovery and Data Mining. He is a speaker on Business Analytics, Data Mining, Data Science, and Big Data. He recently left his role as the Chief Data Officer at Barclays Bank.
Philip S. Yu is an American computer scientist and professor of information technology at the University of Illinois at Chicago. He is a prolific author, holds over 300 patents, and is known for his work in the field of data mining.
Martin L. Kersten was a computer scientist with research focus on database architectures, query optimization and their use in scientific databases. He was an architect of the MonetDB system, an open-source column store for data warehouses, online analytical processing (OLAP) and geographic information systems (GIS). He has been (co-) founder of several successful spin-offs of the Centrum Wiskunde & Informatica (CWI).
Gregory I. Piatetsky-Shapiro is a data scientist and the co-founder of the KDD conferences, and co-founder and past chair of the Association for Computing Machinery SIGKDD group for Knowledge Discovery, Data Mining and Data Science. He is the founder and president of KDnuggets, a discussion and learning website for Business Analytics, Data Mining and Data Science.
Discovering communities in a network, known as community detection/discovery, is a fundamental problem in network science, which attracted much attention in the past several decades. In recent years, with the tremendous studies on big data, another related but different problem, called community search, which aims to find the most likely community that contains the query node, has attracted great attention from both academic and industry areas. It is a query-dependent variant of the community detection problem. A detailed survey of community search can be found at ref., which reviews all the recent studies
Arthur Zimek is a professor in data mining, data science and machine learning at the University of Southern Denmark in Odense, Denmark.
An associative classifier (AC) is a kind of supervised learning model that uses association rules to assign a target value. The term associative classification was coined by Bing Liu et al., in which the authors defined a model made of rules "whose right-hand side are restricted to the classification class attribute".
Frequent pattern discovery is part of knowledge discovery in databases, Massive Online Analysis, and data mining; it describes the task of finding the most frequent and relevant patterns in large datasets. The concept was first introduced for mining transaction databases. Frequent patterns are defined as subsets that appear in a data set with frequency no less than a user-specified or auto-determined threshold.
Gautam Das is a computer scientist in the field of databases research. He is an ACM Fellow and IEEE Fellow.
Hui Xiong is a data scientist. He is a distinguished professor at Rutgers University and a distinguished guest professor at the University of Science and Technology of China (USTC).
Volker Markl is a German computer scientist and database systems researcher.
