Mining Data Streams with Periodically changing
DistributionsYingying Tao, Tamer Ozsu CIKM’09
Supervisor Dr Koh Speaker Nonhlanhla Shongwe
April 26, 2010
Preview Introduction
Challenge
Method
DMM framework
Distance Function Selection
Experiments
Conclusion
Introduction Mining stream for knowledge mining such as
Clustering Classification Frequent patterns discovery , has become important
Important characteristic of unbounded data stream is that the underlying distributions can show important changes over time, leading to dynamic data streams.
Challenge The problem of mining dynamic data streams
Balance accuracy with efficiency – highly accurate mining techniques are generally computationally expensive
Some question to ask: for dynamic data streams Is the distribution changes entirely random and unpredictable Is it possible for the distribution changes to follow certain
patterns?
Method Propose a method for mining dynamic data streams
Where important observed distributions patterns are stored And compared new detected changes with these patterns
Method Two streams with the same distribution
Mining results such as List of all frequent items / itemset for frequent patterns discovery Set of clusters / classes for clustering and classification should be the
same If distribution change is detected and a match is found
Possible to skip the re-mining process Directly output the mining results for the archived distribution
This is called the match-and-reuse strategy
Method Issues to be resolved
Pattern selection Selecting and storing important distributions that have a high
probability of occurring in the future Pattern representation
Storing each pattern succinctly Matching
Efficient procedure for rapid data streams with high accuracy
DMM framework DMM framework stands for Detect, Match and Mine
Consist of four sequences Choosing representative set Change detection Pattern matching Stream mining
All processes are independent
DMM framework Window model
Generate reference window (choosing representative set)
Change detection
Distribution matching (Pattern matching)
Choosing important distribution
Window model Two windows on Stream S
Time-based Defines the time intervals Denoted by Wt
Called observation window Implemented as a tumbling window Moves forward at each clock tick
Window model cont’s Count-based
Contains a sub stream with fixed number of elements Denoted by Wr Called reference window
The size of the reference window (|Wr|) and time intervals of Wt are predefined values
DMM framework Window model
Generate reference window (choosing representative set)
Change detection
Distribution matching (Pattern matching)
Choosing important distribution
Generate reference window (choosing representative set)
Wr stores a set of data elements that represents a current distribution of S
The size needs to be small due to memory limitations Inaccurate results if we use a small data set to represent a
distribution if the distribution is complicated Due to this problem, use a dynamic reference window (Wr)
Generate reference window (choosing representative set)
Merge and select process Dynamic reference window (Wr) Merge Wt and Wr to get a larger substream |Wr|+|Wt| Select |Wr| elements from the merged window (Wr +Wt) and
replace the stream in Wr by the new set Merge and select process is triggered every time Wt tumbles
Generate reference window (choosing representative set)
Generate reference window (choosing representative set)
Selecting representative set: Two-step sampling approach First-step sampling approach
Estimate the density function of Wr + Wt
K= kernel function h= smoothing parameter (bandwidth) si= data element in Wr + Wt
Generate reference window (choosing representative set)
Selecting representative set: Two-step sampling approach K is set to (Standard Gaussian function mean = 0 variance = 1)
Then the density function
h=value between 0 or 1
Generate reference window (choosing representative set)
Selecting representative set: Two-step sampling approach With the density function we are able to estimate the “shape” of
the current distribution
X-axis is the = value of the data s (v(s)) in Wr +Wt Y-axis is the probability (p(v)) for all data values
Generate reference window (choosing representative set)
Selecting representative set: Two-step sampling approach Second-step sampling approach
Generate reference window (choosing representative set)
Selecting representative set: Two-step sampling approach Second-step sampling approach
First calculate the start and end values for each partition
DMM framework Window model
Generate reference window (choosing representative set)
Change detection
Distribution matching (Pattern matching)
Choosing important distribution
Change detection Online change detection technique that is not restricted to
specific stream processing application
Wt tumbles, the change detection procedure is triggered
Compare the distributions of substreams in both Wr and Wt windows If the distance is greater than the predefined maximum matching
distance, then a distribution change is flagged
DMM framework Window model
Generate reference window (choosing representative set)
Change detection
Distribution matching (Pattern matching)
Choosing important distribution
Distribution matching (Pattern matching)
We use the appropriate distance measure to check their similarity
If a match is found, then the persevered mining results are outputted
The maximum predefined maximum matching is important Smaller implies a higher accuracy Larger increases the possibility of a new distribution to match a
pattern in the preserved set
DMM framework Window model
Generate reference window (choosing representative set)
Change detection
Distribution matching (Pattern matching)
Choosing important distribution
Choosing important distribution
Use heuristic rules Distribution that have occurred in the stream for more times are
more important The longer a distribution lasts in the streams lifespan the more
important it is Distribution that has mining results with higher accuracy is more
important that a distributions with less accurate mining results
Distance Function Selection Dynamic Time Wrapping (DTW), Longest Common Subsequence
(LCSS), Edit Distance on Real Sequence (EDR) and Relativized Discrepancy (RD)
A proper distance function that can be used with DMM Efficient , with the ability to stretching
Experiments Change detection
Kernel density approach (KD) Distance function-based approach(DF)
Experiments Distribution matching evaluation
Data from Tropical Atmosphere Ocean Sea surface temperatures. 12 218 streams each with a length of 962
Experiments Efficiency with and without DMM
Adopt a popular decision tree-based clustering technique VFDT to cluster the temperatures Best decision tree generators for dynamic data streams
Time is reduced by 31.3%
Conclusion Introduced a DMM framework to mine dynamic data streams
Window model Generate reference window (choosing representative set) Change detection Distribution matching (Pattern matching) Choosing important distribution
Experiments that showing DMM performs better
Thank you for your attention
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