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CHAMELEON:A Hierarchical Clustering Algorithm Using

Dynamic Modeling

Paper presentation in data mining class

Presenter : 許明壽 ; 蘇建仲Data : 2001/12/18

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About this paper …

Department of Computer Science and Engineering , University of Minnesota George Karypis Eui-Honh (Sam) Han Vipin Kumar

IEEE Computer Journal - Aug. 1999

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Outline

Problems definition Main algorithm Keys features of CHAMELEON Experiment and related worked Conclusion and discussion

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Problems definition

Clustering Intracluster similarity is maximized Intercluster similarity is minimized

Problems of existing clustering algorithms Static model constrain Breakdown when clusters that are of diverse shap

es， densities， and sizes Susceptible to noise , outliers , and artifacts

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Static model constrain Data space constrain

K means , PAM … etc Suitable only for data in metric spaces

Cluster shape constrain K means , PAM , CLARANS

Assume cluster as ellipsoidal or globular and are similar sizes Cluster density constrain

DBScan Points within genuine cluster are density-reachable and point across

different clusters are not Similarity determine constrain

CURE , ROCK Use static model to determine the most similar cluster to merge

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Partition techniques problem

(a) Clusters of widely different sizes (b) Clusters with convex shapes

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Hierarchical technique problem (1/2) The {(c) , (d)} will be choose to merge when we only consider closeness

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Hierarchical technique problem (2/2) The {(a) , (c)} will be choose to merge when we only consider inter-

connectivity

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Main algorithm

Two phase algorithm PHASE I

Use graph partitioning algorithm to cluster the data items into a large number of relatively small sub-clusters.

PHASE II Uses an agglomerative hierarchical clustering

algorithm to find the genuine clusters by repeatedly combining together these sub-clusters.

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Framework

Construct

Sparse Graph Partition the Graph

Merge Partition

Final Clusters

Data Set

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Keys features of CHAMELEON

Modeling the data Modeling the cluster similarity Partition algorithms Merge schemes

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Terms

Arguments needed K

K-nearest neighbor graph MINSIZE

The minima size of initial cluster TRI

Threshold of related inter-connectivity TRC

Threshold of related intra-connectivity α

Coefficient for weight of RI and RC

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Modeling the data

K-nearest neighbor graph approach Advantages

Data points that are far apart are completely disconnected in the Gk

Gk capture the concept of neighborhood dynamically

The edge weights of dense regions in Gk tend to be large and the edge weights of sparse tend to be small

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Example of k-nearest neighbor graph

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Modeling the clustering similarity (1/2)

Relative interconnectivity

Relative closeness2

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Modeling the clustering similarity (2/2)• If related is considered , {(c) , (d)} will be merged

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Partition algorithm (PHASE I)

What Finding the initial sub-clusters

Why RI and RC can’t be accurately calculated for clusters contai

ning only a few data points

How Utilize multilevel graph partitioning algorithm (hMETIS)

Coarsening phase Partitioning phase Uncoarsening phase

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Partition algorithm (cont.)

Initial all points belonging to the same cluster

Repeat until (size of all clusters < MINSIZE) Select the largest cluster and use hMETIS to bise

ct Post scriptum

Balance constrain Spilt Ci into CiA and CiB and each sub-clusters contains

at least 25% of the node of Ci

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What Merging sub-clusters using a dynamic framework

How Finding and merging the pair of sub-clusters that are the

most similar Scheme 1

Scheme 2

Merge schemes (Phase II)

),(*),( jiji CCRCCCRI

RIji TCCRI ),( and RCji TCCRC ),(

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Experiment and related worked

Introduction of CURE Introduction of DBSCAN Results of experiment Performance analysis

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Introduction of CURE (1/n)

Clustering Using Representative points

1. Properties : Fit for non-spherical shapes. Shrinking can help to dampen the effects of outliers. Multiple representative points chosen for non-spherical Each iteration , representative points shrunk ratio related

to merge procedure by some scattered points chosen Random sampling in data sets is fit for large databases

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Introduction of CURE (2/n)

2. Drawbacks : Partitioning method can not prove data points chosen are

good. Clustering accuracy with respect to the parameters below :

(1) Shrink factor s : CURE always find the right clusters by range of s values from 0.2 to 0.7.

(2) Number of representative points c : CURE always found right clusters for value of c greater than 10.

(3) Number of Partitions p : with as many as 50 partitions , CURE always discovered the desired clusters.

(4) Random Sample size r : (a) for sample size up to 2000 , clusters found poor quality (b) from 2500 sample points and above , about 2.5% of the

data set size , CURE always correctly find the clusters.

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3. Clustering algorithm : Representative points

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• Merge procedure

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Introduction of DBSCAN (1/n) Density Based Spatial Clustering of Application With

Noise1. Properties :

Can discovery clusters of arbitrary shape. Each cluster with a typical density of points which is higher

than outside of cluster. The density within the areas of noise is lower than the dens

ity in any of the clusters. Input the parameters MinPts only Easy to implement in C++ language using R*-tree Runtime is linear depending on the number of points. Time complexity is O(n * log n)

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Introduction of DBSCAN (2/n)

2. Drawbacks : Cannot apply to polygons. Cannot apply to high dimensional feature spaces. Cannot process the shape of k-dist graph with multi-featur

es. Cannot fit for large database because no method applied t

o reduce spatial database.

3. Definitions Eps-neighborhood of a point p

NEps(p)={q€D | dist(p,q)<=Eps} Each cluster with MinPts points

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Introduction of DBSCAN (3/n)

4. p is directly density-reachable from q(1) p€ NEps(q) and

(2) | NEps(q) | >=MinPts (core point condition) We know directly density-reachable is symmetric when p

and q both are core point , otherwise is asymmetric if one core point and one border point.

5. p is density-reachable from q if there is a chain of points between p and q

Density-reachable is transitive , but not symmetric Density-reachable is symmetric for core points.

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Introduction of DBSCAN (4/n)

6. A point p is density-connected to a point q if there is a point s such that both p and q are density-reachable from s.

Density-connected is symmetric and reflexive relation A cluster is defined to be a set of density-connected points

which is maximal density-reachability. Noise is the set of points not belong to any of clusters.

7. How to find cluster C ? Maximality

∆ p , q : if p€ C and q is density-reachable from p , then q € C Connectivity

∆ p , q € C : p is density-connected to q

8. How to find noises ? ∆ p , if p is not belong to any clusters , then p is noise point

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Results of experiment

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Performance analysis (1/2) The time of construct the k-nearest neighbor

Low-dimensional data sets based on k-d trees , overall complexity of O(n log n)

High-dimensional data sets based on k-d trees not applicable , overall complexity of O(n2)

Finding initial sub-clusters Obtains m clusters by repeated partitioning successively s

maller graphs , overall computational complexity is O(n log (n/m))

Is bounded by O(n log n) A faster partitioning algorithm to obtain the initial m cluster

s in time O(n+m log m) using multilevel m-way partitioning algorithm

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Performance analysis (2/2)

Merging sub-clusters using a dynamic framework

The time of compute the internal inter-connectivity and internal closeness for each initial cluster is which is O(nm)

The time of the most similar pair of clusters to merge is O(m2 log m) by using a heap-based priority queue

So overall complexity of CHAMELEON’s is O(n log n + nm + m2 log m)

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Conclusion and discussion

Dynamic model with related interconnectivity and closeness

This paper ignore the issue of scaling to large data

Other graph representation methodology?? Other Partition algorithm??