References for mining from streaming data
While reading a lower quality paper on the subject I found these references worth tracking down. Essentially the idea is that you can make one-pass through the data and must produce statistics of the data that are approximate in nature, ideally with bounded approximation error.
Gibbons et al 1997: Fast Incremental Maintenance of Approximate HistogramsIncremental maintenance of histograms primarily for database query planners Manku and Motwani 2002 Approximate frequency counts over data streams.
They show algorithms called sticky sampling and lossy counting with proven error bounds. Zhu et al. 2002 StatStream: Statistical Monitoring of Thousands of Data Streams in Real Time
Basic stats and high correlations between all pairs of data streams Cormode and Muthukrishnan's 2003 What's Hot and What's Not: Tracking Most Frequent Items Dynamically
Introduced groupTest probabilistic Monte Carlo algorithm for frequent item tracking Metwally et al 2005 Efficient Computation of Frequent and Top-k Elements in Data Streams
Uses counters to monitor data streams with a new stream-summary data structure Cheng et al 2005 Time-Decaying Bloom Filters for Data Streams with Skewed Distributions
Dampened Bloom-filters for frequent items Three papers on frequent itemsets (different than frequent items):Jiang and Gruenwald have a pair of 2006 papers
Research Issues in Data Stream Association Rule Mining
Survey paper of issues and previous resultsCFI-Stream: Mining Closed Frequent Itemsets in Data Streams
New stream based itemset miner
Tsai and Chen 2009 Mining Frequent Itemsets for data streams over Weighted Sliding Windows
Apriori like itemset miner on windows of data with differential weighting
(9/19) Note: corrected the StatStream link - hat tip @dataspora
