a summary to current clustering methods and optics: ordering points to identify the clustering...
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A Summary to Current Clustering Methods and OPTICS: Ordering Points To Identify the Clustering Structure
Presented byHo Wai Shing
Overview Introduction Current Clustering Techniques OPTICS Discussions
Introduction What is clustering?
Given: a dataset with N points in a d -dimensional space
Task: find a natural partitioning of the points into a number (k ) of closely related groups (clusters) and noise
Introduction Example Application
To find similar electronic parts from their design blue-prints:
use Fourier transform to transform contours of parts into coefficients
do clustering on the coefficients discuss this later in the talk
Introduction What are the main concerns?
Efficiency Effectiveness Scalability Interactivity
Current Techniques can be classified into groups
hierarchical vs partitioning bottom-up merging vs flat partitioning
centroid-based vs density-based 1 or more representative points for a
cluster
Several Clustering Algorithms k -mean BIRCH DBSCAN CURE / C2P OPTICS
k-mean partitions the space into k clusters each cluster is represented by the
mean of the points belong to this cluster
iteratively refines the k representative points until reaching a local minimal on total distances within clusters
k-mean the clusters must be convex, and
should have similar size (may not be the case in real data)
need to scan the database many times (slow)
easily disturbed by outliers (every point counts in calculating the mean)
an example dataset
BIRCH use CF-Tree to summarize the data
points so that everything are in memory points will be merged to a leaf entry of
the CF-Tree if they are similar points will be stored in an “extension” if
no similar leaves can be found build clusters over those leaves
instead of the original data points
an example dataset
entries in CF-Tree Leaves (contains N, LS, SS)
an example dataset
… …
entries in CF-Tree Leaves (contains N, LS, SS)
BIRCH basically hierarchical one of the fastest algorithm available scan the data only once can remove some outliers can be used as a pre-clustering step the result depends on inserting order
DBSCAN the first density-based algorithm
without grid clusters = collections of density-
connected points definitions:
directly density-reachable density-reachable density-connected
r is directly density-reachable from q
DBSCAN start with an arbitrary point, perform k-NN (k-nearest-neighbor)
search for that point if it is dense then we grow that
point into a cluster find another point until we exhaust
all the points
DBSCAN good:
can find arbitrary shaped clusters intuitive definition of clusters reasonable complexity if index is available
for k-NN search bad:
difficult to determine input parameters Eps and MinPts
suffers from “Chaining Effect”
The Chaining Problem
chain
CURE instead of using all points in
calculating the distance between a point and a cluster like DBSCAN, CURE uses a set of representative points within a cluster
this could reduce the chaining effect
CURE diagram: no longer chains
actual points points are close,but representatives ain’t
C2P much better than CURE in terms of
efficiency (O(n2lgn) to O(nlgn + m2lgm)) accomplished by a O(nlgn) pre-
clustering phase add links between all the points and their
nearest neighbours condense each connected graph into a
single point repeat until m points remains
OPTICS Ordering Points To Identify the
Clustering Structure in SIGMOD 99 by Ankerst et al. A generalization of DBSCAN + a
visualization technique
OPTICS Motivation:
input parameters (e.g., Eps) are difficult to be determined
one global parameter setting may not fit all the clusters
it’s good to allow users to have flexibility in selecting clusters
OPTICS definitions
core-distance of a point p the distance between the point p and its
MinPts’th neighbour reachability-distance of a point p
w.r.t. another point o the distance between o and p, with a
lower bound of core-dist(o)
OPTICS start from an arbitrary point, sorts the
points according to the reachability-distance
this sorting can be used to produce density-based clusters with 0 < Eps < Epsinput
Reachability plot can be used to provide a good visualization tool for analyzing clusters
OPTICS reachability plot
OPTICS 16-d reachability plot
Discussions All the methods described above
fail at high-dimensional cases “The curse of dimensionality”
distances between all the points are nearly the same
grids are usually not dense (O(2d) grids vs O(n) points)), clusters tend to be divided by grids
no efficient indices for k-NN search
Discussions key observation:
not all dimensions are meaningful in clustering
some clusters may exist under a subset of dimensions while the others exist under another subset of dimensions
leads to: feature selection subspace clustering / projected clustering
References M Ankerst, M M Breunig and H-P Kriegel, J Sander, OPTICS: Ordering
Points To Identify the Clustering Structure, SIGMOD’99 T Zhang, R Ramakrishnan and M Livny, BIRCH: An Efficient Data
Clustering Method for Very Large Databases, SIGMOD’96 S Guha, R Rastogi and K Shim, CURE: An Efficent Clustering Algorithm
for Large Databases SIGMOD’98 C C Aggarwal and P S Yu, Finding Generalized Projected Clusters in
High Dimensional Spaces, SIGMOD’00 R Agrawal, J Gehrke, D Gunopulos and P Raghavan, Automatic
Subspace Clustering of High Dimensional Data for Data Mining Applications, SIGMOD’98
M Ester, H-P Kriegel, J Sander and X Xu, A Density-Based Algorithm for Discovering Clusters in Large Spatial Database with Noise, KDD’96
A Nanopoulos, Y Theodoridis and Y Manolopoulos, C2P: Clustering based on Closest Pairs, VLDB’01
Questions?
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