han/eick: clustering ii 1 clustering part2 continued 1. birch skipped 2. density-based clustering...
TRANSCRIPT
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Han/Eick: Clustering II 1
Clustering Part2 continued
1. BIRCH skipped
2. Density-based Clustering --- DBSCAN and DENCLUE
3. GRID-based Approaches --- STING and CLIQUE
4. SOM skipped
5. Cluster Validation other set of transparencies
6. Outlier/Anomaly Detection other set of transparencies
Slides in red will be used for Part1.
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Han/Eick: Clustering II 2
BIRCH (1996)
Birch: Balanced Iterative Reducing and Clustering using Hierarchies, by Zhang, Ramakrishnan, Livny (SIGMOD’96)
Incrementally construct a CF (Clustering Feature) tree, a hierarchical data structure for multiphase clustering Phase 1: scan DB to build an initial in-memory CF
tree (a multi-level compression of the data that tries to preserve the inherent clustering structure of the data)
Phase 2: use an arbitrary clustering algorithm to cluster the leaf nodes of the CF-tree
Scales linearly: finds a good clustering with a single scan and improves the quality with a few additional scans
Weakness: handles only numeric data, and sensitive to the order of the data record.
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Han/Eick: Clustering II 3
Clustering Feature Vector
Clustering Feature: CF = (N, LS, SS)
N: Number of data points
LS: Ni=1=Xi
SS: Ni=1=Xi
2
1 2 3 4 5 6 7 8 9 10
0
1
2
3
4
5
6
7
8
9
10
CF = (5, (16,30),(54,190))
(3,4)(2,6)(4,5)(4,7)(3,8)
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Han/Eick: Clustering II 4
CF TreeCF1
child1
CF3
child3
CF2
child2
CF6
child6
CF1
child1
CF3
child3
CF2
child2
CF5
child5
CF1 CF2 CF6prev next CF1 CF2 CF4
prev next
B = 7
L = 6
Root
Non-leaf node
Leaf node Leaf node
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Han/Eick: Clustering II 5
Chapter 8. Cluster Analysis
What is Cluster Analysis? Types of Data in Cluster Analysis A Categorization of Major Clustering Methods Partitioning Methods Hierarchical Methods Density-Based Methods Grid-Based Methods Model-Based Clustering Methods Outlier Analysis Summary
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Han/Eick: Clustering II 6
Density-Based Clustering Methods
Clustering based on density (local cluster criterion), such as density-connected points or based on an explicitly constructed density function
Major features: Discover clusters of arbitrary shape Handle noise Usually one scan Need density estimation parameters
Several interesting studies: DBSCAN: Ester, et al. (KDD’96) OPTICS: Ankerst, et al (SIGMOD’99). DENCLUE: Hinneburg & D. Keim (KDD’98) CLIQUE: Agrawal, et al. (SIGMOD’98)
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Han/Eick: Clustering II 7
DBSCAN Two parameters:
Eps: Maximum radius of the neighbourhood MinPts: Minimum number of points in an Eps-
neighbourhood of that point NEps(p): {q belongs to D | dist(p,q) <= Eps} Directly density-reachable: A point p is directly
density-reachable from a point q wrt. Eps, MinPts if 1) p belongs to NEps(q) 2) core point condition:
|NEps (q)| >= MinPts
pq
MinPts = 5
Eps = 1 cm
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Han/Eick: Clustering II 8
Density-Based Clustering: Background (II)
Density-reachable: A point p is density-reachable
from a point q wrt. Eps, MinPts if there is a chain of points p1, …, pn, p1 = q, pn = p such that pi+1 is directly density-reachable from pi
Density-connected A point p is density-connected
to a point q wrt. Eps, MinPts if there is a point o such that both, p and q are density-reachable from o wrt. Eps and MinPts.
p
qp1
p q
o
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Han/Eick: Clustering II 9
DBSCAN: Density Based Spatial Clustering of Applications with Noise
Relies on a density-based notion of cluster: A cluster is defined as a maximal set of density-connected points
Discovers clusters of arbitrary shape in spatial databases with noise
Core
Border
Outlier
Eps = 1cm
MinPts = 5
Density reachablefrom core point
Not density reachablefrom core point
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Han/Eick: Clustering II 10
DBSCAN: The Algorithm
Arbitrary select a point p
Retrieve all points density-reachable from p wrt Eps and MinPts.
If p is a core point, a cluster is formed.
If p ia not a core point, no points are density-reachable from p and DBSCAN visits the next point of the database.
Continue the process until all of the points have been processed.
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Han/Eick: Clustering II 11
DENCLUE: using density functions
DENsity-based CLUstEring by Hinneburg & Keim (KDD’98)
Major features Solid mathematical foundation Good for data sets with large amounts of noise Allows a compact mathematical description of
arbitrarily shaped clusters in high-dimensional data sets
Significant faster than existing algorithm (faster than DBSCAN by a factor of up to 45)
But needs a large number of parameters
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Han/Eick: Clustering II 12
Influence function: describes the impact of a data point within its neighborhood.
Overall density of the data space can be calculated as the sum of the influences of all data points.
Clusters can be determined mathematically by identifying density attractors; object that are associated with the same density attractor belong to the same cluster
Density attractors are local maximal of the overall density function.
Uses grid-cells to speed up computations; only data points in neighboring grid-cells are used to determine the density for a point.
Denclue: Technical Essence
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Han/Eick: Clustering II 13
DENCLUE Influence Function and its Gradient
Example
N
i
xxdD
Gaussian
i
exf1
2
),(2
2
)(
N
i
xxd
iiD
Gaussian
i
exxxxf1
2
),(2
2
)(),(
f x y eGaussian
d x y
( , )( , )
2
22
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Han/Eick: Clustering II 14
Example: Density Computation
D={x1,x2,x3,x4}
fDGaussian(x)= influence(x1) + influence(x2) + influence(x3) +
influence(x4)=0.04+0.06+0.08+0.6=0.78
x1
x2
x3
x4x 0.6
0.08
0.06
0.04
y
Remark: the density value of y would be larger than the one for x
2
2
2
),(
),(inf yxd
eyxluence
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Han/Eick: Clustering II
Example Non-Parametric DE in R Demo Rcode: setwd("C:\\Users\\C. Eick\\Desktop")a<-read.csv("c8.csv")require("spatstat")require("ppp")d<-data.frame(a=a[,1],b=a[,2],c=a[,3])plot(d$a,d$b)w <- owin(poly=list (list(x=c(0,530,701,640,0),y=c(0,42,20,400,420)),list(x=c(320,430,310), y=c(215,200,190)),list(x=c(10,70,170,20), y=c(200,220,170, 175))) )z<-ppp(d[,1],d[,2],window=w, marks=factor(d[,3]))plot(z)summary(z)q<-quadratcount(z, nx=12,ny=10)plot(q)den<-density(z, sigma=80)plot(den)den<-density(z, sigma=30)plot(den)den<-density(z, sigma=15)plot(den)den<-density(z, sigma=12)plot(den)den<-density(z, sigma=10)plot(den)den<-density(z, sigma=4)plot(den)
documentation for function ‘density’: http://127.0.0.1:28030/library/spatstat/html/density.ppp.html
15
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Han/Eick: Clustering II 16
Density Attractor
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Han/Eick: Clustering II 17
Examples of DENCLUE Clusters
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Han/Eick: Clustering II 18
Basic Steps DENCLUE Algorithms
1. Determine density attractors2. Associate data objects with density
attractors using hill climbing ( initial clustering)
3. Merge the initial clusters further relying on a hierarchical clustering approach (optional)
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Han/Eick: Clustering II 19
Density-based Clustering: Pros and Cons
+: can discover clusters of arbitrary shape+: not sensitive to outliers and supports outlier detection+: can handle noise +-: medium algorithm complexities-: finding good density estimation parameters is frequently difficult; more difficult to use than K-means. -: usually, do not do well in clustering high-dimensional datasets. -: cluster models are not well understood (yet)
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Han/Eick: Clustering II 20
Chapter 8. Cluster Analysis
What is Cluster Analysis? Types of Data in Cluster Analysis A Categorization of Major Clustering Methods Partitioning Methods Hierarchical Methods Density-Based Methods Grid-Based Methods Model-Based Clustering Methods Outlier Analysis Summary
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Han/Eick: Clustering II 21
Steps of Grid-based Clustering Algorithms
Basic Grid-based Algorithm1. Define a set of grid-cells2. Assign objects to the appropriate grid
cell and compute the density of each cell.
3. Eliminate cells, whose density is below a certain threshold t.
4. Form clusters from contiguous (adjacent) groups of dense cells (usually minimizing a given objective function).
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Han/Eick: Clustering II 22
Advantages of Grid-based Clustering Algorithms
fast: No distance computations Clustering is performed on summaries
and not individual objects; complexity is usually O(#-populated-grid-cells) and not O(#objects)
Easy to determine which clusters are neighboring
Shapes are limited to union of rectangular grid-cells
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Han/Eick: Clustering II 23
Grid-Based Clustering Methods
Several interesting methods (in addition to the basic grid-based algorithm) STING (a STatistical INformation Grid approach)
by Wang, Yang and Muntz (1997) CLIQUE: Agrawal, et al. (SIGMOD’98)
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Han/Eick: Clustering II 24
STING: A Statistical Information Grid Approach
Wang, Yang and Muntz (VLDB’97) The spatial area area is divided into rectangular
cells There are several levels of cells corresponding to
different levels of resolution
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Han/Eick: Clustering II 25
STING: A Statistical Information Grid Approach (2)
Main contribution of STING is the proposal of a data structure that can be used for many purposes (e.g. SCMRG, BIRCH kind of uses it)
The data structure is used to form clusters based on queries
Each cell at a high level is partitioned into a number of smaller cells in the next lower level
Statistical info of each cell is calculated and stored beforehand and is used to answer queries
Parameters of higher level cells can be easily calculated from parameters of lower level cell
count, mean, s, min, max type of distribution—normal, uniform, etc.
Use a top-down approach to answer spatial data queries Clusters are formed by merging cells that match a given
query description ( next slide)
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Han/Eick: Clustering II 26
STING: Query Processing(3)
Used a top-down approach to answer spatial data queries1. Start from a pre-selected layer—typically with a small
number of cells2. From the pre-selected layer until you reach the bottom
layer do the following: For each cell in the current level compute the confidence
interval indicating a cell’s relevance to a given query; If it is relevant, include the cell in a cluster If it irrelevant, remove cell from further consideration otherwise, look for relevant cells at the next lower layer
3. Combine relevant cells into relevant regions (based on grid-neighborhood) and return the so obtained clusters as your answers.
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Han/Eick: Clustering II 27
STING: A Statistical Information Grid Approach (3)
Advantages: Query-independent, easy to parallelize,
incremental update O(K), where K is the number of grid cells at
the lowest level Can be used in conjunction with a grid-based
clustering algorithm Disadvantages:
All the cluster boundaries are either horizontal or vertical, and no diagonal boundary is detected
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Han/Eick: Clustering II 28
Subspace Clustering
Clustering in very high-dimensional spaces is very difficult
High dimensional attribute spaces tend to be sparseit is hard to find any clusters
It is very difficult to create summaries from clusters in very difficult
This creates the motivation for subspace clustering: Find interesting subspaces (areas that are dense
with respect to the attributes belonging to the subspace)
Find clusters for each interesting Remark: multiple, overlapping clusters might be
obtained; basically one clustering for each subspace.
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Han/Eick: Clustering II 29
CLIQUE (Clustering In QUEst) Agrawal, Gehrke, Gunopulos, Raghavan
(SIGMOD’98). Automatically identifying subspaces of a high
dimensional data space that allow better clustering than original space
CLIQUE can be considered as both density-based and grid-based It partitions each dimension into the same
number of equal length interval It partitions an m-dimensional data space into
non-overlapping rectangular units A unit is dense if the fraction of total data points
contained in the unit exceeds the input model parameter
A cluster is a maximal set of connected dense units within a subspace
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Han/Eick: Clustering II 30
CLIQUE: The Major Steps
Partition the data space and find the number of points that lie inside each cell of the partition.
Identify the subspaces that contain clusters using the Apriori principle
Identify clusters: Determine dense units in all subspaces of
interests Determine connected dense units in all
subspaces of interests.
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Han/Eick: Clustering II 31
Sala
ry
(10,
000)
20 30 40 50 60age
54
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Han/Eick: Clustering II 32
Strength and Weakness of CLIQUE
Strength It automatically finds subspaces of the highest
dimensionality such that high density clusters exist in those subspaces
It is insensitive to the order of records in input and does not presume some canonical data distribution
It scales linearly with the size of input and has good scalability as the number of dimensions in the data increases
Weakness The accuracy of the clustering result may be
degraded at the expense of simplicity of the method
Quite expensive
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Han/Eick: Clustering II 33
Self-organizing feature maps (SOMs)
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
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Han/Eick: Clustering II 34
References (1)
R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Automatic subspace clustering of high dimensional data for data mining applications. SIGMOD'98
M. R. Anderberg. Cluster Analysis for Applications. Academic Press, 1973. M. Ankerst, M. Breunig, H.-P. Kriegel, and J. Sander. Optics: Ordering points to
identify the clustering structure, SIGMOD’99. P. Arabie, L. J. Hubert, and G. De Soete. Clustering and Classification. World
Scietific, 1996 M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for
discovering clusters in large spatial databases. KDD'96. M. Ester, H.-P. Kriegel, and X. Xu. Knowledge discovery in large spatial
databases: Focusing techniques for efficient class identification. SSD'95. D. Fisher. Knowledge acquisition via incremental conceptual clustering. Machine
Learning, 2:139-172, 1987. D. Gibson, J. Kleinberg, and P. Raghavan. Clustering categorical data: An
approach based on dynamic systems. In Proc. VLDB’98. S. Guha, R. Rastogi, and K. Shim. Cure: An efficient clustering algorithm for
large databases. SIGMOD'98. A. K. Jain and R. C. Dubes. Algorithms for Clustering Data. Printice Hall, 1988.
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Han/Eick: Clustering II 35
References (2)
L. Kaufman and P. J. Rousseeuw. Finding Groups in Data: an Introduction to Cluster Analysis. John Wiley & Sons, 1990.
E. Knorr and R. Ng. Algorithms for mining distance-based outliers in large datasets. VLDB’98.
G. J. McLachlan and K.E. Bkasford. Mixture Models: Inference and Applications to Clustering. John Wiley and Sons, 1988.
P. Michaud. Clustering techniques. Future Generation Computer systems, 13, 1997.
R. Ng and J. Han. Efficient and effective clustering method for spatial data mining. VLDB'94.
E. Schikuta. Grid clustering: An efficient hierarchical clustering method for very large data sets. Proc. 1996 Int. Conf. on Pattern Recognition, 101-105.
G. Sheikholeslami, S. Chatterjee, and A. Zhang. WaveCluster: A multi-resolution clustering approach for very large spatial databases. VLDB’98.
W. Wang, Yang, R. Muntz, STING: A Statistical Information grid Approach to Spatial Data Mining, VLDB’97.
T. Zhang, R. Ramakrishnan, and M. Livny. BIRCH : an efficient data clustering method for very large databases. SIGMOD'96.