Sponsored by the Association for the Advancement of Artificial Intelligence
The Second International Conference on Knowledge Discovery and Data Mining (KDD-96) was held August 2–4, 1996, in Portland, Oregon Knowledge discovery in databases (KDD), also referred to as data mining, is an area of common interest to researchers in machine discovery, statistics, databases, knowledge acquisition, machine learning, data visualization, high performance computing, and knowledge-based systems. The rapid growth of data and information has created a need and an opportunity for extracting knowledge from databases, and both researchers and application developers have been responding to that need. KDD applications have been developed for astronomy, biology, finance, insurance, marketing, medicine, and many other fields.
The first international conference on Knowledge Discovery and Data Mining (KDD-95), held in Montréal in August 1995, was an outstanding success, attracting over 340 participants. The second international conference will follow up the success of KDD-95 by bringing together researchers and application developers from different areas focusing on unifying themes.
With the dramatic advances in data acquisition and storage technologies, the problem of how to turn raw data into useful information has become one of the most daunting problems facing modern society. Having reached sizes that defy even partial examination by humans, modern databases and collections of data sets are literally drowning their users in data. This data firehose phenomenon appears in a wide spectrum of fields including retail and corporate marketing, medical and healthcare, financial markets, and engineering, manufacturing, and science data analysis. Knowledge discovery in databases (KDD) and data mining are areas of common interest to researchers in AI, pattern recognition, statistics, databases, knowledge acquisition, data visualization, high performance computing, and expert systems. KDD-96 follows on the success of KDD-95 held in Montréal, and continues the tradition of the KDD workshops from 1989 – 1994, by bringing together researchers and application developers from different areas, and focusing on unifying themes such as the automated extraction of patterns and models from databases, statistical inference, issues of scaling to massive data sets, the use of domain knowledge, managing uncertainty, interactive (human-oriented) presentation, and applications. The KDD conference also includes invited talks, demo and poster sessions, and panel discussions.
Evangelos Simoudis and Jiawei Han
Program Committee Cochairs
General Conference Chair