Distributed cooperative mining for information consortia

Satoshi Morinaga, Kenji Yamanishi, Junnichi Takeuchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We consider the situation where a number of agents are distributed and each of them collects a data sequence generated according to an unknown probability distribution. Here each of the distributions is specified by common parameters and individual parameters e.g., a normal distribution with an identical mean and a different variance. Here we introduce a notion of an information consortium, which is a framework where the agents cannot show raw data to one another, but they like to enjoy significant information gain for estimating the respective distributions. Such an information consortium has recently received much interest in a broad range of areas including financial risk management, ubiquitous network mining, etc. In this paper we are concerned with the following three issues: 1) how to design a collaborative strategy for agents to estimate the respective distributions in the information consortium, 2) characterizing when each agent has a benefit in terms of information gain for estimating its distribution or information loss for predicting future data, and 3) charracterizing how much benefit each agent obtains. In this paper we yield a statistical formulation of information consortia and solve all of the above three problems for a general form of probability distributions. Specifically we propose a basic strategy for cooperative estimation and derive a necessary and sufficient condition for each agent to have a significant benefit.

Original languageEnglish
Title of host publicationProceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03
Number of pages6
Publication statusPublished - 2003
Externally publishedYes
Event9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03 - Washington, DC, United States
Duration: Aug 24 2003Aug 27 2003


Other9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03
Country/TerritoryUnited States
CityWashington, DC

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems


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