My8clst

Cluster Analysis ,[object Object],[object Object],[object Object],modified by Donghui Zhang

Chapter 7. Cluster Analysis ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

What is Cluster Analysis? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

General Applications of Clustering ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Examples of Clustering Applications ,[object Object],[object Object],[object Object],[object Object],[object Object]

What Is Good Clustering? ,[object Object],[object Object],[object Object],[object Object],[object Object]

Requirements of Clustering in Data Mining ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Major Clustering Approaches ,[object Object],[object Object],[object Object],[object Object],[object Object]

Partitioning Algorithms: Basic Concept ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

The K-Means Clustering Method ,[object Object],[object Object],[object Object],[object Object]

The K-Means Clustering Method ,[object Object],0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 Assign each objects to most similar center 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 Update the cluster means 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 Update the cluster means K=2 Arbitrarily choose K object as initial cluster center reassign

Comments on the K-Means Method ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Variations of the K-Means Method ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Hierarchical Clustering ,[object Object],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)

AGNES (Agglomerative Nesting) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

A Dendrogram Shows How the Clusters are Merged Hierarchically ,[object Object],[object Object]

DIANA (Divisive Analysis) ,[object Object],[object Object],[object Object],[object Object]

More on Hierarchical Clustering Methods ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

BIRCH (1996) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Balanced Iterative Reducing and Clustering using Hierarchies

Clustering Feature Vector Clustering Feature: CF = (N, LS, SS) N : Number of data points LS:  N i=1 X i SS:  N i=1 (X i ) 2 CF = (5, (16,30),244) (3,4) (2,6) (4,5) (4,7) (3,8)

Some Characteristics of CF ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

CF-Tree in BIRCH ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

CF Tree CF 1 child 1 CF 3 child 3 CF 2 child 2 CF 5 child 5 CF 1 CF 2 CF 6 prev next CF 1 CF 2 CF 4 prev next B = 7 L = 6 Root Non-leaf node Leaf node Leaf node CF 1 child 1 CF 3 child 3 CF 2 child 2 CF 6 child 6

CF-Tree Construction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

CURE (Clustering Using REpresentatives ) ,[object Object],[object Object],[object Object],[object Object],[object Object]

Motivation: K-means is not good

[object Object],AGNES + d min is not good, either

Cure: The Basic Version ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Representative points ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Cure: for large data set ,[object Object],[object Object],[object Object],[object Object],[object Object],A representative is close to it.

Data Partitioning and Clustering ,[object Object],[object Object],[object Object],x x x y y y y x y x

Cure: Shrinking Representative Points ,[object Object],[object Object],x y x y

CHAMELEON ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Example of CHAMELEON Construct Sparse Graph Partition the Graph Merge Partition Final Clusters Data Set

Density-Based Clustering Methods ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Density Concepts ,[object Object],[object Object],[object Object],[object Object]

Density-Based Clustering: Background ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],p q MinPts = 5 Eps = 1 cm

Density-Based Clustering: Background (II) ,[object Object],[object Object],[object Object],[object Object],p q p 1 p q o

DBSCAN: Density Based Spatial Clustering of Applications with Noise ,[object Object],[object Object],Core Border Outlier Eps = 1cm MinPts = 5

DBSCAN: The Algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object]

OPTICS: A Cluster-Ordering Method (1999) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

OPTICS: Some Extension from DBSCAN ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],D p2 MinPts = 5  = 3 cm Max (core-distance (o), d (o, p)) r(p1, o) = 2.8cm. r(p2,o) = 4cm o o p1

Reachability-distance Cluster-order of the objects undefined ‘

Density-Based Cluster analysis: OPTICS & Its Applications

DENCLUE: Using density functions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

[object Object],[object Object],[object Object],[object Object],[object Object],Denclue: Technical Essence

Gradient: The steepness of a slope ,[object Object]

Grid-Based Clustering Method ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

STING: A Statistical Information Grid Approach ,[object Object],[object Object],[object Object]

STING: A Statistical Information Grid Approach (2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

STING: A Statistical Information Grid Approach (3) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

WaveCluster (1998) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

WaveCluster (1998) ,[object Object],[object Object],[object Object],[object Object],[object Object]

Wavelet Transform ,[object Object],[object Object],[object Object]

WaveCluster (1998) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

CLIQUE (Clustering In QUEst) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

CLIQUE: The Major Steps ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Salary (10,000) 20 30 40 50 60 age 5 4 3 1 2 6 7 0  = 3 20 30 40 50 60 age 5 4 3 1 2 6 7 0 Vacation(week) age Vacation Salary 30 50

Strength and Weakness of CLIQUE ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Model-Based Clustering Methods ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

COBWEB Clustering Method A classification tree

More on Statistical-Based Clustering ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Other Model-Based Clustering Methods ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Model-Based Clustering Methods

Self-organizing feature maps (SOMs) ,[object Object],[object Object],[object Object],[object Object],[object Object]

What Is Outlier Discovery? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Outlier Discovery: Statistical Approaches ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Outlier Discovery: Distance-Based Approach ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Outlier Discovery: Deviation-Based Approach ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Problems and Challenges ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Constraint-Based Clustering Analysis ,[object Object]

Clustering With Obstacle Objects Taking obstacles into account Not Taking obstacles into account

Summary ,[object Object],[object Object],[object Object],[object Object],[object Object]

References (1) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

References (2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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