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November 26, 2014
Data Mining: Concepts and
Chapter 7. Cluster
Analysis
1. What is Cluster Analysis?
2. Types of Data in Cluster Analysis
3. A Categorization of Major Clustering Methods
4. Partitioning Methods
5. Hierarchical Methods
6. Density-Based Methods
7. Grid-Based Methods
8. Model-Based Methods
9. Clustering High-Dimensional Data
10.Constraint-Based Clustering
11.Outlier Analysis
12.Summary
November 26, 2014
Data Mining: Concepts and
Hierarchical Clustering
- (^) Use distance matrix as clustering criteria. This method does not require the number of clusters k as an input, but needs a termination condition Step 0 Step 1^ Step 2^ Step 3^ Step 4 b d c e a a b d e c d e a b c d e Step 4 Step 3^ Step 2^ Step 1^ Step 0 agglomerative (AGNES) divisive (DIANA)
November 26, 2014
Data Mining: Concepts and
Dendrogram: Shows How the Clusters are Merged Decompose data objects into a several levels of nested partitioning (tree of clusters), called a dendrogram. A clustering of the data objects is obtained by cutting the dendrogram at the desired level, then each connected component forms a cluster.
November 26, 2014
Data Mining: Concepts and
DIANA (Divisive Analysis)
- (^) Introduced in Kaufmann and Rousseeuw (1990)
- (^) Implemented in statistical analysis packages, e.g., Splus
- (^) Inverse order of AGNES
- (^) Eventually each node forms a cluster on its own 0
November 26, 2014
Data Mining: Concepts and
Recent Hierarchical Clustering Methods
- (^) Major weakness of agglomerative clustering methods
- (^) do not scale well: time complexity of at least O ( n^2 ), where n is the number of total objects
- (^) can never undo what was done previously
- (^) Integration of hierarchical with distance-based clustering - (^) BIRCH (1996): uses CF-tree and incrementally adjusts the quality of sub-clusters - (^) ROCK (1999): clustering categorical data by neighbor and link analysis - (^) CHAMELEON (1999): hierarchical clustering using dynamic modeling
November 26, 2014
Data Mining: Concepts and
CHAMELEON: Hierarchical Clustering Using Dynamic Modeling (1999)
- (^) CHAMELEON: by G. Karypis, E.H. Han, and V. Kumar’ 99
- (^) Measures the similarity based on a dynamic model
- (^) Two clusters are merged only if the interconnectivity and closeness (proximity) between two clusters are high relative to the internal interconnectivity of the clusters and closeness of items within the clusters
- (^) Cure ignores information about interconnectivity of the objects, Rock ignores information about the closeness of two clusters
- (^) A two-phase algorithm
- Use a graph partitioning algorithm: cluster objects into a large number of relatively small sub-clusters
- Use an agglomerative hierarchical clustering algorithm: find the genuine clusters by repeatedly combining these sub-clusters
November 26, 2014
Data Mining: Concepts and
CHAMELEON (Clustering Complex Objects)
November 26, 2014
Data Mining: Concepts and
CURE Clustering Using Representatives Use many points to represent a cluster instead of only one Points will be well scattered
November 26, 2014
Data Mining: Concepts and
CURE Algorithm
November 26, 2014
Data Mining: Concepts and
CURE for Large Databases
