Intro to Data mining

 Answer the following questions. Please ensure to use the Author, YYYY APA citations with any content brought into the assignment.  

For sparse data, discuss why considering only the presence of non-zero values might give a more accurate view of the objects than considering the actual magnitudes of values. When would such an approach not be desirable?

Describe the change in the time complexity of K-means as the number of clusters to be found increases.

Discuss the advantages and disadvantages of treating clustering as an optimization problem. Among other factors, consider efficiency, non-determinism, and whether an optimization-based approach captures all types of clusterings that are of interest.

What is the time and space complexity of fuzzy c-means? Of SOM? How do these complexities compare to those of K-means?

Explain the difference between likelihood and probability.

Give an example of a set of clusters in which merging based on the closeness of clusters leads to a more natural set of clusters than merging based on the strength of connection (interconnectedness) of clusters.

Data Mining
Cluster Analysis: Advanced Concepts
and Algorithms
Lecture Notes for Chapter 9
Introduction to Data Mining
by
Tan, Steinbach, Kumar

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 *

Hierarchical Clustering: Revisited
Creates nested clusters

Agglomerative clustering algorithms vary in terms of how the proximity of two clusters are computed
MIN (single link): susceptible to noise/outliers
MAX/GROUP AVERAGE:
may not work well with non-globular clusters

CURE algorithm tries to handle both problems

Often starts with a proximity matrix
A type of graph-based algorithm

Uses a number of points to represent a cluster

Representative points are found by selecting a constant number of points from a cluster and then “shrinking” them toward the center of the cluster

Cluster similarity is the similarity of the closest pair of representative points from different clusters
CURE: Another Hierarchical Approach

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

CURE
Shrinking representative points toward the center helps avoid problems with noise and outliers
CURE is better able to handle clusters of arbitrary shapes and sizes

Experimental Results: CURE
Picture from CURE, Guha, Rastogi, Shim.

Experimental Results: CURE
Picture from CURE, Guha, Rastogi, Shim.
(centroid)
(single link)

CURE Cannot Handle Differing Densities
Original Points

CURE

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Graph-Based Clustering
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 as starting with this graph
In the simplest case, clusters are connected components in the graph.

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

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

Sparsification in the Clustering Process

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

Limitations of Current Merging Schemes
Closeness schemes will merge (a) and (b)
(a)
(b)
(c)
(d)
Average connectivity schemes will merge (c) and (d)

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 property is 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

One of the areas of application is spatial data

Characteristics of Spatial Data Sets
Clusters are defined as densely populated regions of the space
Clusters have arbitrary shapes, orientation, and non-uniform sizes
Difference in densities across clusters and variation in density within clusters
Existence of special artifacts (streaks) and noise
The clustering algorithm must address the above characteristics and also require minimal supervision.

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., is a sub-cluster of a “real” cluster

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

Experimental Results: CHAMELEON

Experimental Results: CHAMELEON

Experimental Results: CURE (10 clusters)

Experimental Results: CURE (15 clusters)

Experimental Results: CHAMELEON

Experimental Results: CURE (9 clusters)

Experimental Results: CURE (15 clusters)

SNN graph: the weight of an edge is the number of shared neighbors between vertices given that the vertices are connected
Shared Near Neighbor Approach

i
j

i
j

4

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

ROCK (RObust Clustering using linKs)
Clustering algorithm for data with categorical and Boolean attributes
A pair of points is defined to be neighbors if their similarity is greater than some threshold
Use a hierarchical clustering scheme to cluster the data.

Obtain a sample of points from the data set
Compute the link value for each set of points, i.e., transform the original similarities (computed by Jaccard coefficient) into similarities that reflect the number of shared neighbors between points
Perform an agglomerative hierarchical clustering on the data using the “number of shared neighbors” as similarity measure and maximizing “the shared neighbors” objective function
Assign the remaining points to the clusters that have been found

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

When Jarvis-Patrick Works Reasonably Well
Original Points

Jarvis Patrick Clustering
6 shared neighbors out of 20

Smallest threshold, T, that does not merge clusters.
Threshold of T – 1
When Jarvis-Patrick Does NOT Work Well

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

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 place 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)

SNN Density

a) All Points b) High SNN Density
c) Medium SNN Density d) Low SNN Density

SNN Clustering Can Handle Differing Densities
Original Points

SNN Clustering

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SNN Clustering Can Handle Other Difficult Situations

Finding Clusters of Time Series In Spatio-Temporal Data

SNN Clusters of SLP.
SNN Density of Points on the Globe.

Features and 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

26 SLP Clusters via Shared Nearest Neighbor Clustering (100 NN, 1982-1994)
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