Clustering Lecture 8: MapReduce
Clustering Lecture 8: MapReduce
Jing Gao
SUNY Buffalo
1
Outline
? Basics
? Motivation, definition, evaluation
? Methods
? Partitional ? Hierarchical ? Density-based ? Mixture model ? Spectral methods
? Advanced topics
? Clustering ensemble ? Clustering in MapReduce ? Semi-supervised clustering, subspace clustering, co-clustering,
etc.
2
Big Data EveryWhere
? Lots of data is being collected and warehoused
? Web data, e-commerce ? purchases at department/
grocery stores ? Bank/Credit Card
transactions ? Social Network
3
Divide and Conquer
"Work"
Partition
w1
"worker"
r1
w2
"worker"
r2
w3
"worker"
r3
"Result"
Combine
4
Distributed Grep
Very big data
Split data Split data Split data
Split data
grep grep grep
grep
matches
matches
matches
cat
matches
All matches
5
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