1. Clustering and Object Similarity Evaluation
©Jiawei Han and Micheline Kamber
with Additions and Modifications by Ch. Eick
Organization for COSC 6340:
What is Clustering?
Object Similarity Measurement
K-Means Clustering Algorithm
Hierarchical Clustering

2. Examples of Clustering Applications
Marketing: Help marketers discover distinct groups in their customer bases, and then use this knowledge to develop targeted marketing programs
Land use: Identification of areas of similar land use in an earth observation database
Insurance: Identifying groups of motor insurance policy holders with a high average claim cost
City-planning: Identifying groups of houses according to their house type, value, and geographical location
Earth-quake studies: Observed earth quake epicenters should be clustered along continent faults

3. Requirements of Clustering in Data Mining
Scalability
Ability to deal with different types of attributes
Discovery of clusters with arbitrary shape
Minimal requirements for domain knowledge to determine input parameters
Able to deal with noise and outliers
Insensitive to order of input records
High dimensionality
Incorporation of user-specified constraints
Interpretability and usability

4. Data Structures for Clustering
Data matrix
(n objects,
p attributes)
(Dis)Similarity matrix
(nxn)

5. Quality Evaluation of Clusters
Dissimilarity/Similarity metric: Similarity is expressed in terms of a normalized distance function d, which is typically metric; typically: d (oi, oj) = 1 - d (oi, oj)
There is a separate “quality” function that measures the “goodness” of a cluster.
The definitions of similarity functions are usually very different for interval-scaled, boolean, categorical, ordinal and ratio variables.
Weights should be associated with different variables based on applications and data semantics.
It is hard to define “similar enough” or “good enough”
the answer is typically highly subjective.

6. Challenges in Obtaining Object Similarity Measures
Many Types of Variables
Interval-scaled variables
Binary variables and nominal variables
Ordinal variables
Ratio-scaled variables
Objects are characterized by variables belonging to different types (mixture of variables)

7. Case Study: Patient Similarity
The following relation is given (with tuples):
Patient(ssn, weight, height, cancer-sev, eye-color, age)
Attribute Domains
ssn: 9 digits
weight between 30 and 650; mweight=158 sweight=24.20
height between 0.30 and 2.20 in meters; mheight=1.52 sheight=19.2
cancer-sev: 4=serious 3=quite_serious 2=medium 1=minor
eye-color: {brown, blue, green, grey }
age: between 3 and 100; mage=45 sage=13.2
Task: Define Patient Similarity

8. Generating a Global Similarity Measure from Single Variable Similarity Measures
Assumption: A database may contain up to six types of variables: symmetric binary, asymmetric binary, nominal, ordinal, interval and ratio.
Standardize variable and associate similarity measure di with the standardized i-th variable and determine weight wi of the i-th variable.
Create the following global (dis)similarity measure d:

9. A Methodology to Obtain a Similarity Matrix
Understand Variables
Remove (non-relevant and redundant) Variables
(Standardize and) Normalize Variables (typically using z-scores or variable values are transformed to numbers in [0,1])
Associate (Dis)Similarity Measure df/df with each Variable
Associate a Weight (measuring its importance) with each Variable
Compute the (Dis)Similarity Matrix
Apply Similarity-based Data Mining Technique (e.g. Clustering, Nearest Neighbor, Multi-dimensional Scaling,…)

10. Interval-scaled Variables
Standardize data using z-scores
Calculate the mean absolute deviation:
where
Calculate the standardized measurement (z-score)
Using mean absolute deviation is more robust than using standard deviation

11. Normalization in [0,1]
Problem: If non-normalized variables are used the maximum distance between two values can be greater than 1.
Solution: Normalize interval-scaled variables using
where minf denotes the minimum value and maxf denotes the maximum value of the f-th attribute in the data set and s is constant that is choses depending on the similarity measure (e.g. if Manhattan distance is used s is chosen to be 1).

12. Other Normalizations Goal:Limit the maximum distance to 1
Start using a distance measure df(x,y)
Determine the maximum distance dmaxf that can occur for two values of the f-th attribute (e.g. dmaxf=maxf-minf ).
Define df(x,y)=1- (df(x,y)/ dmaxf)
Advantage: Negative similarities cannot occur.

13. Similarity Between Objects
Distances are normally used to measure the similarity or dissimilarity between two data objects
Some popular ones include: Minkowski distance:
where i = (xi1, xi2, …, xip) and j = (xj1, xj2, …, xjp) are two p-dimensional data objects, and q is a positive integer
If q = 1, d is Manhattan distance

14. Similarity Between Objects (Cont.)
If q = 2, d is Euclidean distance:
Properties
d(i,j)  0
d(i,i) = 0
d(i,j) = d(j,i)
d(i,j)  d(i,k) + d(k,j)
Also one can use weighted distance, parametric Pearson product moment correlation, or other disimilarity measures.

15. Similarity with respect to a Set of Binary Variables
A contingency table for binary data
Object j
Object i
Ignores agree-
ments in O’s
Considers agree-
ments in 0’s and 1’s
to be equivalent.

16. Similarity between Binary Variable Sets
Example
gender is a symmetric attribute
the remaining attributes are asymmetric binary
let the values Y and P be set to 1, and the value N be set to 0

17. Nominal Variables
A generalization of the binary variable in that it can take more than 2 states, e.g., red, yellow, blue, green
Method 1: Simple matching
m: # of matches, p: total # of variables
Method 2: use a large number of binary variables
creating a new binary variable for each of the M nominal states

18. Ordinal Variables An ordinal variable can be discrete or continuous
order is important (e.g. UH-grade, hotel-rating)
Can be treated like interval-scaled
replacing xif by their rank:
map the range of each variable onto [0, 1] by replacing the f-th variable of i-th object by
compute the dissimilarity using methods for interval-scaled variables

19. Ratio-Scaled Variables
Ratio-scaled variable: a positive measurement on a nonlinear scale, approximately at exponential scale, such as AeBt or Ae-Bt
Methods:
treat them like interval-scaled variables — not a good choice! (why?)
apply logarithmic transformation
yif = log(xif)
treat them as continuous ordinal data treat their rank as interval-scaled.

20. Case Study --- Normalization
Patient(ssn, weight, height, cancer-sev, eye-color, age)
Attribute Relevance: ssn no; eye-color minor; other major
Attribute Normalization:
ssn remove!
weight between 30 and 650; mweight=158 sweight=24.20; transform to zweight= (xweight-158)/24.20 (alternatively, zweight=(xweight-30)/620));
height normalize like weight!
cancer_sev: 4=serious 3=quite_serious 2=medium 1=minor; transform 4 to 1, 3 to 2/3, 2 to 1/3, 1 to 0 and then normalize like weight!
age: normalize like weight!

21. Case Study --- Weight Selection and Similarity Measure Selection
Patient(ssn, weight, height, cancer-sev, eye-color, age)
For normalized weight, height, cancer_sev, age values use Manhattan distance function; e.g.:
dweight(w1,w2)= 1 - | ((w1-158)/24.20 ) - ((w2-158)/24.20) |
For eye-color use: deye-color(c1,c2)= if c1=c2 then 1 else 0
Weight Assignment: 0.2 for eye-color; 1 for all others
Final Solution --- chosen Similarity Measure d:
Let o1=(s1,w1,h1,cs1,e1,a1) and o2=(s2,w2,h2,cs2,e2,a2)
d(o1,o2):= (dweight(w1,w2) + dheight(h1,h2) + dcancer-sev(cs1,cs2) + dage(a1,a2) + 0.2* deye-color(e1,e2) ) /4.2

22. Major Clustering Approaches
Partitioning algorithms: Construct various partitions and then evaluate them by some criterion
Hierarchy algorithms: Create a hierarchical decomposition of the set of data (or objects) using some criterion
Density-based: based on connectivity and density functions
Grid-based: based on a multiple-level granularity structure
Model-based: A model is hypothesized for each of the clusters and the idea is to find the best fit of that model to each other

23. Partitioning Algorithms: Basic Concept
Partitioning method: Construct a partition of a database D of n objects into a set of k clusters
Given a k, find a partition of k clusters that optimizes the chosen partitioning criterion
Global optimal: exhaustively enumerate all partitions
Heuristic methods: k-means and k-medoids algorithms
k-means (MacQueen’67): Each cluster is represented by the center of the cluster
k-medoids or PAM (Partition around medoids) (Kaufman & Rousseeuw’87): Each cluster is represented by one of the objects in the cluster

24. The K-Means Clustering Method
Given k, the k-means algorithm is implemented in 4 steps:
Partition objects into k nonempty subsets
Compute seed points as the centroids of the clusters of the current partition. The centroid is the center (mean point) of the cluster.
Assign each object to the cluster with the nearest seed point.
Go back to Step 2, stop when no more new assignment.

25. The K-Means Clustering Method
Example

26. Comments on the K-Means Method
Strength
Relatively efficient: O(tkn), where n is # objects, k is # clusters, and t is # iterations. Normally, k, t << n.
Often terminates at a local optimum. The global optimum may be found using techniques such as: deterministic annealing and genetic algorithms
Weakness
Applicable only when mean is defined, then what about categorical data?
Need to specify k, the number of clusters, in advance
Unable to handle noisy data and outliers
Not suitable to discover clusters with non-convex shapes

27. 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
agglomerative
(AGNES)
divisive
(DIANA)

28. A Dendrogram Shows How the Clusters are Merged Hierarchically
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.

29. Self-organizing feature maps (SOMs)
SOM employ neural network techniques for clustering
Clustering is also performed by having several units competing for the current object
The unit whose weight vector is closest to the current object wins
The winner and its neighbors learn by having their weights adjusted
SOMs are believed to resemble processing that can occur in the brain
Useful for visualizing high-dimensional data in 2- or 3-D space

30. Problems and Challenges
Considerable progress has been made in scalable clustering methods
Partitioning: k-means, k-medoids, CLARANS
Hierarchical: BIRCH, CURE
Density-based: DBSCAN, CLIQUE, OPTICS
Grid-based: STING, WaveCluster
Model-based: Autoclass, Denclue, Cobweb
Current clustering techniques do not address all the requirements adequately
Constraint-based clustering analysis: Constraints exist in data space (bridges and highways) or in user queries

31. Summary Object Similarity & Clustering
Cluster analysis groups objects based on their similarity and has wide applications
Appropriate similarity measures have to be chosen for various types of variables and combined into a global similarity measure.
Clustering algorithms can be categorized into partitioning methods, hierarchical methods, density-based methods, grid-based methods, and model-based methods
Methods to measure, compute, and learn object similarity are quite important, not only for clustering, but also for nearest neighbor approaches, information retrieval in general, and for data visualization.

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