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chap8_advanced_cluster_analysis1.pptxData Mining Cluster Analysis: Advanced Concepts and Algorithms
Lecture Notes for Chapter 8
Introduction to Data Mining, 2nd Edition
by
Tan, Steinbach, Karpatne, Kumar
02/14/2018 Introduction to Data Mining, 2nd Edition ‹#›
Outline
Prototype-based
Fuzzy c-means
Mixture Model Clustering
Self-Organizing Maps
Density-based
Grid-based clustering
Subspace clustering
Graph-based
Chameleon
Jarvis-Patrick
Shared Nearest Neighbor (SNN)
Characteristics of Clustering Algorithms
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Hard (Crisp) vs Soft (Fuzzy) Clustering
Hard (Crisp) vs. Soft (Fuzzy) clustering
For soft clustering allow point to belong to more than one cluster
For K-means, generalize objective function
: weight with which object xi belongs to cluster
To minimize SSE, repeat the following steps:
Fix and determine w(cluster assignment)
Fixw and recompute
Hard clustering:w {0,1}
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Soft (Fuzzy) Clustering: Estimating Weights
SSE(x) is minimized when wx1 = 1, wx2 = 0
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Fuzzy C-means
Objective function
: weight with which object belongs to cluster
a power for the weight not a superscript and controls how “fuzzy” the clustering is
To minimize objective function, repeat the following:
Fix and determinew
Fixwand recompute
Fuzzy c-means clustering:w[0,1]
Bezdek, James C. Pattern recognition with fuzzy objective function algorithms. Kluwer Academic Publishers, 1981.
p: fuzzifier (p > 1)
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Fuzzy C-means
SSE(x) is minimized when wx1 = 0.9, wx2 = 0.1
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SSE(x)
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Fuzzy C-means
Objective function:
Initialization: choose the weights wij randomly
Repeat:
Update centroids:
Update weights:
p: fuzzifier (p > 1)
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Fuzzy K-means Applied to Sample Data
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An Example Application: Image Segmentation
Modified versions of fuzzy c-means have been used for image segmentation
Especially fMRI images (functional magnetic resonance images)
References
Gong, Maoguo, Yan Liang, Jiao Shi, Wenping Ma, and Jingjing Ma. "Fuzzy c-means clustering with local information and kernel metric for image segmentation." Image Processing, IEEE Transactions on 22, no. 2 (2013): 573-584.
From left to right: original images, fuzzy c-means, EM, BCFCM
Ahmed, Mohamed N., Sameh M. Yamany, Nevin Mohamed, Aly A. Farag, and Thomas Moriarty. "A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data." Medical Imaging, IEEE Transactions on 21, no. 3 (2002): 193-199.
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Hard (Crisp) vs Soft (Probabilistic) Clustering
Idea is to model the set of data points as arising from a mixture of distributions
Typically, normal (Gaussian) distribution is used
But other distributions have been very profitably used
Clusters are found by estimating the parameters of the statistical distributions
Can use a k-means like algorithm, called the Expectation-Maximization (EM) algorithm, to estimate these parameters
Actually, k-means is a special case of this approach
Provides a compact representation of clusters
The probabilities with which point belongs to each cluster provide a functionality similar to fuzzy clustering.
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Probabilistic Clustering: Example
Informal example: consider modeling the points that generate the following histogram.
Looks like a combination of two normal (Gaussian) distributions
Suppose we can estimate the mean and standard deviation of each normal distribution.
This completely describes the two clusters
We can compute the probabilities with which each point belongs to each cluster
Can assign each point to the cluster (distribution) for which it is most probable.
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Probabilistic Clustering: EM Algorithm
Initialize the parameters
Repeat
For each point, compute its probability under each distribution
Using these probabilities, update the parameters of each distribution
Until there is no change
Very similar to K-means
Consists of assignment and update steps
Can use random initialization
Problem of local minima
For normal distributions, typically use K-means to initialize
If using normal distributions, can find elliptical as well as spherical shapes.
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Probabilistic Clustering: Updating Centroids
Update formula for weights assuming an estimate for statistical parameters
Very similar to the fuzzy k-means formula
Weights are probabilities
Weights are not raised to a power
Probabilities calculated using Bayes rule:
Need to assign weights to each cluster
Weights may not be equal
Similar to prior probabilities
Can be estimated:
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More Detailed EM Algorithm
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Probabilistic Clustering Applied to Sample Data
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Probabilistic Clustering: Dense and Sparse Clusters
?
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Problems with EM
Convergence can be slow
Only guarantees finding local maxima
Makes some significant statistical assumptions
Number of parameters for Gaussian distribution grows as O(d2), d the number of dimensions
Parameters associated with covariance matrix
K-means only estimates cluster means, which grow as O(d)
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Alternatives to EM
Method of moments / Spectral methods
ICML 2014 workshop bibliography https://sites.google.com/site/momentsicml2014/bibliography
Markov chain Monte Carlo (MCMC)
Other approaches
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SOM: Self-Organizing Maps
Self-organizing maps (SOM)
Centroid based clustering scheme
Like K-means, a fixed number of clusters are specified
However, the spatial relationship of clusters is also specified, typically as a grid
Points are considered one by one
Each point is assigned to the closest centroid
Other centroids are updated based on their nearness to the closest centroid
Kohonen, Teuvo, and Self-Organizing Maps. "Springer series in information sciences." Self-organizing maps 30 (1995).
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SOM: Self-Organizing Maps
Updates are weighted by distance
Centroids farther away are affected less
The impact of the updates decreases with each time
At some point the centroids will not change much
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SOM can be viewed as a type of dimensionality reduction
If a two-dimensional grid is used, the results can be visualized
SOM: Self-Organizing Maps
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SOM Clusters of LA Times Document Data
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Another SOM Example: 2D Points
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Issues with SOM
Computational complexity
Locally optimal solution
Grid is somewhat arbitrary
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Grid-based Clustering
A type of density-based clustering
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Subspace Clustering
Until now, we found clusters by considering all of the attributes
Some clusters may involve only a subset of attributes, i.e., subspaces of the data
Example:
When k-means is used to find document clusters, the resulting clusters can typically be characterized by 10 or so terms
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Example
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Example
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Example
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Example
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Clique Algorithm - Overview
A grid-based clustering algorithm that methodically finds subspace clusters
Partitions the data space into rectangular units of equal volume
Measures the density of each unit by the fraction of points it contains
A unit is dense if the fraction of overall points it contains is above a user specified threshold,
A cluster is a group of collections of contiguous (touching) dense units
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Clique Algorithm
It is impractical to check each volume unit to see if it is dense since there is exponential number of such units
Monotone property of density-based clusters:
If a set of points forms a density based cluster in k dimensions, then the same set of points is also part of a density based cluster in all possible subsets of those dimensions
Very similar to Apriori algorithm
Can find overlapping clusters
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Clique Algorithm
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Limitations of Clique
Time complexity is exponential in number of dimensions
Especially if “too many” dense units are generated at lower stages
May fail if clusters are of widely differing densities, since the threshold is fixed
Determining appropriate threshold and unit interval length can be challenging
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Denclue (DENsity CLUstering)
Based on the notion of kernel-density estimation
Contribution of each point to the density is given by an influence or kernel function
Overall density is the sum of the contributions of all points
Formula and plot of Gaussian Kernel
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Example of Density from Gaussian Kernel
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DENCLUE Algorithm
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DENCLUE Algorithm
Find the density function
Identify local maxima (density attractors)
Assign each point to the density attractor
Follow direction of maximum increase in density
Define clusters as groups consisting of points associated with density attractor
Discard clusters whose density attractor has a density less than a user specified minimum,
Combine clusters connected by paths of points that are connected by points with density above
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Graph-Based Clustering: General Concepts
Graph-Based clustering uses the proximity graph
Start with the proximity matrix
Consider each point as a node in a graph
Each edge between two nodes has a weight which is the proximity between the two points
Initially the proximity graph is fully connected
MIN (single-link) and MAX (complete-link) can be viewed in graph terms
In the simplest case, clusters are connected components in the graph.
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CURE Algorithm: Graph-Based Clustering
Agglomerative hierarchical clustering algorithms vary in terms of how the proximity of two clusters are computed
MIN (single link)
susceptible to noise/outliers
MAX (complete link)/GROUP AVERAGE/Centroid/Ward’s:
may not work well with non-globular clusters
CURE algorithm tries to handle both problems
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Represents a cluster using multiple representative points
Representative points are found by selecting a constant number of points from a cluster
First representative point is chosen to be the point furthest from the center of the cluster
Remaining representative points are chosen so that they are farthest from all previously chosen points
CURE Algorithm
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“Shrink” representative points toward the center of the cluster by a factor,
Shrinking representative points toward the center helps avoid problems with noise and outliers
Cluster similarity is the similarity of the closest pair of representative points from different clusters
CURE Algorithm
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CURE Algorithm
Uses agglomerative hierarchical scheme to perform clustering;
= 0: similar to centroid-based
= 1: somewhat similar to single-link
CURE is better able to handle clusters of arbitrary shapes and sizes
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Experimental Results: CURE
Picture from CURE, Guha, Rastogi, Shim.
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Experimental Results: CURE
Picture from CURE, Guha, Rastogi, Shim.
(centroid)
(single link)
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CURE Cannot Handle Differing Densities
Original Points
CURE
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Graph-Based Clustering: Chameleon
Based on several key ideas
Sparsification of the proximity graph
Partitioning the data into clusters that are relatively pure subclusters of the “true” clusters
Merging based on preserving characteristics of clusters
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Graph-Based Clustering: Sparsification
The amount of data that needs to be processed is drastically reduced
Sparsification can eliminate more than 99% of the entries in a proximity matrix
The amount of time required to cluster the data is drastically reduced
The size of the problems that can be handled is increased
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Graph-Based Clustering: Sparsification …
Clustering may work better
Sparsification techniques keep the connections to the most similar (nearest) neighbors of a point while breaking the connections to less similar points.
The nearest neighbors of a point tend to belong to the same class as the point itself.
This reduces the impact of noise and outliers and sharpens the distinction between clusters.
Sparsification facilitates the use of graph partitioning algorithms (or algorithms based on graph partitioning algorithms)
Chameleon and Hypergraph-based Clustering
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Sparsification in the Clustering Process
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Limitations of Current Merging Schemes
Existing merging schemes in hierarchical clustering algorithms are static in nature
MIN or CURE:
Merge two clusters based on their closeness (or minimum distance)
GROUP-AVERAGE:
Merge two clusters based on their average connectivity
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Limitations of Current Merging Schemes
Closeness schemes will merge (a) and (b)
(a)
(b)
(c)
(d)
Average connectivity schemes will merge (c) and (d)
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Chameleon: Clustering Using Dynamic Modeling
Adapt to the characteristics of the data set to find the natural clusters
Use a dynamic model to measure the similarity between clusters
Main properties are the relative closeness and relative inter-connectivity of the cluster
Two clusters are combined if the resulting cluster shares certain properties with the constituent clusters
The merging scheme preserves self-similarity
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Relative Interconnectivity
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Relative Closeness
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Chameleon: Steps
Preprocessing Step: Represent the data by a Graph
Given a set of points, construct the k-nearest-neighbor (k-NN) graph to capture the relationship between a point and its k nearest neighbors
Concept of neighborhood is captured dynamically (even if region is sparse)
Phase 1: Use a multilevel graph partitioning algorithm on the graph to find a large number of clusters of well-connected vertices
Each cluster should contain mostly points from one “true” cluster, i.e., be a sub-cluster of a “real” cluster
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Chameleon: Steps …
Phase 2: Use Hierarchical Agglomerative Clustering to merge sub-clusters
Two clusters are combined if the resulting cluster shares certain properties with the constituent clusters
Two key properties used to model cluster similarity:
Relative Interconnectivity: Absolute interconnectivity of two clusters normalized by the internal connectivity of the clusters
Relative Closeness: Absolute closeness of two clusters normalized by the internal closeness of the clusters
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Experimental Results: CHAMELEON
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Experimental Results: CURE (10 clusters)
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Experimental Results: CURE (15 clusters)
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Experimental Results: CHAMELEON
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Experimental Results: CURE (9 clusters)
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Experimental Results: CURE (15 clusters)
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Experimental Results: CHAMELEON
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Shared Nearest Neighbor (SNN) graph: the weight of an edge is the number of shared neighbors between vertices given that the vertices are connected
Graph-Based Clustering: SNN Approach
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Creating the SNN Graph
Sparse Graph
Link weights are similarities between neighboring points
Shared Near Neighbor Graph
Link weights are number of Shared Nearest Neighbors
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Jarvis-Patrick Clustering
First, the k-nearest neighbors of all points are found
In graph terms this can be regarded as breaking all but the k strongest links from a point to other points in the proximity graph
A pair of points is put in the same cluster if
any two points share more than T neighbors and
the two points are in each others k nearest neighbor list
For instance, we might choose a nearest neighbor list of size 20 and put points in the same cluster if they share more than 10 near neighbors
Jarvis-Patrick clustering is too brittle
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When Jarvis-Patrick Works Reasonably Well
Original Points
Jarvis Patrick Clustering
6 shared neighbors out of 20
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Smallest threshold, T, that does not merge clusters.
Threshold of T - 1
When Jarvis-Patrick Does NOT Work Well
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SNN Density-Based Clustering
Combines:
Graph based clustering (similarity definition based on number of shared nearest neighbors)
Density based clustering (DBSCAN-like approach)
SNN density measures whether a point is surrounded by similar points (with respect to its nearest neighbors)
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SNN Clustering Algorithm
Compute the similarity matrix This corresponds to a similarity graph with data points for nodes and edges whose weights are the similarities between data points
Sparsify the similarity matrix by keeping only the k most similar neighbors This corresponds to only keeping the k strongest links of the similarity graph
Construct the shared nearest neighbor graph from the sparsified similarity matrix. At this point, we could apply a similarity threshold and find the connected components to obtain the clusters (Jarvis-Patrick algorithm)
Find the SNN density of each Point. Using a user specified parameters, Eps, find the number points that have an SNN similarity of Eps or greater to each point. This is the SNN density of the point
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SNN Clustering Algorithm …
Find the core points Using a user specified parameter, MinPts, find the core points, i.e., all points that have an SNN density greater than MinPts
Form clusters from the core points If two core points are within a “radius”, Eps, of each other they are placed in the same cluster
Discard all noise points All non-core points that are not within a “radius” of Eps of a core point are discarded
Assign all non-noise, non-core points to clusters This can be done by assigning such points to the nearest core point
(Note that steps 4-8 are DBSCAN)
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SNN Density
a) All Points b) High SNN Density
c) Medium SNN Density d) Low SNN Density
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SNN Clustering Can Handle Differing Densities
Original Points
SNN Clustering
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SNN Clustering Can Handle Other Difficult Situations
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Finding Clusters of Time Series In Spatio-Temporal Data
SNN Clusters of SLP.
SNN Density of Points on the Globe.
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Limitations of SNN Clustering
Does not cluster all the points
Complexity of SNN Clustering is high
O( n * time to find numbers of neighbor within Eps)
In worst case, this is O(n2)
For lower dimensions, there are more efficient ways to find the nearest neighbors
R* Tree
k-d Trees
Parameteriziation is not easy
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Characteristics of Data, Clusters, and Clustering Algorithms
A cluster analysis is affected by characteristics of
Data
Clusters
Clustering algorithms
Looking at these characteristics gives us a number of dimensions that you can use to describe clustering algorithms and the results that they produce
© Tan,Steinbach, Kumar Introduction to Data Mining 4/13/2006 78
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High dimensionality
Size of data set
Sparsity of attribute values
Noise and Outliers
Types of attributes and type of data sets
Differences in attribute scales
Properties of the data space
Can you define a meaningful centroid
Characteristics of Data
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Data distribution
Shape
Differing sizes
Differing densities
Poor separation
Relationship of clusters
Types of clusters
Center-based, contiguity-based, density-based
Subspace clusters
Characteristics of Clusters
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Order dependence
Non-determinism
Parameter selection
Scalability
Underlying model
Optimization based approach
Characteristics of Clustering Algorithms
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We assume EM clustering using the Gaussian (normal) distribution.
MIN is hierarchical, EM clustering is partitional.
Both MIN and EM clustering are complete.
MIN has a graph-based (contiguity-based) notion of a cluster, while EM clustering has a prototype (or model-based) notion of a cluster.
MIN will not be able to distinguish poorly separated clusters, but EM can manage this in many situations.
MIN can find clusters of different shapes and sizes; EM clustering prefers globular clusters and can have trouble with clusters of different sizes.
Min has trouble with clusters of different densities, while EM can often handle this.
Neither MIN nor EM clustering finds subspace clusters.
Comparison of MIN and EM-Clustering
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MIN can handle outliers, but noise can join clusters; EM clustering can tolerate noise, but can be strongly affected by outliers.
EM can only be applied to data for which a centroid is meaningful; MIN only requires a meaningful definition of proximity.
EM will have trouble as dimensionality increases and the number of its parameters (the number of entries in the covariance matrix) increases as the square of the number of dimensions; MIN can work well with a suitable definition of proximity.
EM is designed for Euclidean data, although versions of EM clustering have been developed for other types of data. MIN is shielded from the data type by the fact that it uses a similarity matrix.
MIN makes no distribution assumptions; the version of EM we are considering assumes Gaussian distributions.
Comparison of MIN and EM-Clustering
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EM has an O(n) time complexity; MIN is O(n2log(n)).
Because of random initialization, the clusters found by EM can vary from one run to another; MIN produces the same clusters unless there are ties in the similarity matrix.
Neither MIN nor EM automatically determine the number of clusters.
MIN does not have any user-specified parameters; EM has the number of clusters and possibly the weights of the clusters.
EM clustering can be viewed as an optimization problem; MIN uses a graph model of the data.
Neither EM or MIN are order dependent.
Comparison of MIN and EM-Clustering
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Both are partitional.
K-means is complete; DBSCAN is not.
K-means has a prototype-based notion of a cluster; DB uses a density-based notion.
K-means can find clusters that are not well-separated. DBSCAN will merge clusters that touch.
DBSCAN handles clusters of different shapes and sizes; K-means prefers globular clusters.
Comparison of DBSCAN and K-means
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DBSCAN can handle noise and outliers; K-means performs poorly in the presence of outliers
K-means can only be applied to data for which a centroid is meaningful; DBSCAN requires a meaningful definition of density
DBSCAN works poorly on high-dimensional data; K-means works well for some types of high-dimensional data
Both techniques were designed for Euclidean data, but extended to other types of data
DBSCAN makes no distribution assumptions; K-means is really assuming spherical Gaussian distributions
Comparison of DBSCAN and K-means
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K-means has an O(n) time complexity; DBSCAN is O(n^2)
Because of random initialization, the clusters found by K-means can vary from one run to another; DBSCAN always produces the same clusters
DBSCAN automatically determines the number of clusters; K-means does not
K-means has only one parameter, DBSCAN has two.
K-means clustering can be viewed as an optimization problem and as a special case of EM clustering; DBSCAN is not based on a formal model.
Comparison of DBSCAN and K-means
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