文章目录
Hierarchical clustering: revisited
- create 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
CURE: another hierarchical approach
- 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
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
CURE cannot handle differing densities
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
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.)
Limitation 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
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 interconnectivity 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
Phase 2: Use Hierarchical Agglomerative Clustering to merge sub-clusters
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Two clusters are combined if the resulting cluster shares certain properties with the constituent clusters
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Two key properties used to model cluster similarity:
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Relative Interconnectivity: Absolute interconnectivity of two clusters normalized by the internal connectivity of the clusters
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Relative Closeness: Absolute closeness of two clusters normalized by the internal closeness of the clusters
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Shared Near Neighbor Approach
SNN graph: the weight of an edge is the number of shared neighbors between vertices given that the vertices are connected
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.
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Obtain a sample of points from the data set
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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
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Perform an agglomerative hierarchical clustering on the data using the “number of shared neighbors” as similarity measure and maximizing “the shared neighbors” objective function
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Assign the remaining points to the clusters that have been found
Jarvis-Patrick (JP) 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
SNN clustering algorithm
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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 -
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)
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(n^2) $
- For lower dimensions, there are more efficient ways to find
the nearest neighbors- R ∗ R^* R∗ Tree
- k − d k-d k−d Trees
