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What is Cluster?
Cluster is a group of objects that belong to the same class.
In other words the similar object are grouped in one cluster and dissimilar are grouped in other cluster.
Points to Remember
o A cluster of data objects can be treated as a one group.
o While doing the cluster analysis, the set of data into groups based on data similarity and then assign the label to the groups.
o The main advantage of Clustering over classification.
Applications of Cluster Analysis
o Market research, pattern recognition, data analysis, and image processing.
o Characterize their customer groups based on purchasing patterns.
o In field of biology it can be used to derive plant and animal taxonomies, categorize genes with similar functionality and gain insight into structures inherent in populations.
o Clustering also helps in identification of areas of similar land use in an earth observation database. It also helps in the identification of groups of houses in a city according house type, value, and geographic location.
o Clustering also helps in classifying documents on the web for information discovery.
o Clustering is also used in outlier detection applications such as detection of credit card fraud.
o As a data mining function Cluster Analysis serve as a tool to gain insight into the distribution of data to observe characteristics of each cluster.
Requirements of Clustering in Data Mining
Here are the typical requirements of clustering in data mining:
o Scalability - We need highly scalable clustering algorithms to deal with large databases.
o Ability to deal with different kind of attributes - Algorithms should be capable to be applied on any kind of data such as interval based (numerical) data, categorical, binary data.
o Discovery of clusters with attribute shape - The clustering algorithm should be capable of detect cluster of arbitrary shape. The should not be bounded to only distance measures that tend to find spherical cluster of small size.
o High dimensionality - The clustering algorithm should not only be able to handle low-dimensional data but also the high dimensional space.
o Ability to deal with noisy data - Databases contain noisy, missing or erroneous data. Some algorithms are sensitive to such data and may lead to poor quality clusters.
o Interpretability - The clustering results should be interpretable, comprehensible and usable.
The clustering methods can be classified into following categories:
o Partitioning Method
o Hierarchical Method
o Density-based Method
o Grid-Based Method
o Model-Based Method
o Constraint-based Method
Given k, the k-means algorithm is implemented in four steps:
1. Partition objects into k nonempty subsets
2. Compute seed points as the centroids of the clusters of the current partition (the centroid is the center, i.e., mean point, of the cluster)
3. Assign each object to the cluster with the nearest seed point
4. Go back to Step 2, stop when no more new assignment
2. Partitioning Method
Suppose we are given a database of n objects, the partitioning method construct k partition of data.
Each partition will represent a cluster and k≤n. It means that it will classify the data into k groups, which satisfy the following requirements:
o Each group contain at least one object.
o Each object must belong to exactly one group.
K-means, k-medoids, CLARANS
3 Hierarchical Methods
This method creates the hierarchical decomposition of the given set of data objects.:
o Agglomerative Approach
o Divisive Approach
This approach is also known as bottom-up approach. In this we start with each object forming a separate group. It keeps on merging the objects or groups that are close to one another. It keep on doing so until all of the groups are merged into one or until the termination condition holds.
This approach is also known as top-down approach. In this we start with all of the objects in the same cluster. In the continuous iteration, a cluster is split up into smaller clusters. It is down until each object in one cluster or the termination condition holds.
This method is rigid i.e. once merge or split is done, It can never be undone.
Approaches to improve quality of Hierarchical clustering
Here is the two approaches that are used to improve quality of hierarchical clustering:
Perform careful analysis of object linkages at each hierarchical partitioning.
Integrate hierarchical agglomeration by first using a hierarchical agglomerative algorithm to
group objects into microclusters, and then performing macroclustering on the microclusters. Typical methods: Diana, Agnes, BIRCH, ROCK, CAMELEON
BIRCH (1996): uses CF-tree and incrementally adjusts the quality of sub-clusters
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
ROCK (1999): clustering categorical data by neighbor and link analysis
Robust Clustering using links
o Use links to measure similarity/proximity
o Not distance-based
o Computational complexity:
Algorithm: sampling-based clustering
o Draw random sample
o Cluster with links
o Label data in disk
CHAMELEON (1999): hierarchical clustering using dynamic modeling
· Measures the similarity based on a dynamic model
o Two clusters are merged only if the interconnectivity and closeness (proximity) between two clusters are high relative to the internal interconnectivity of the clusters and closeness of items within the clusters
o Cure ignores information about interconnectivity of the objects, Rock ignores information about the closeness of two clusters
A two-phase algorithm
Use a graph partitioning algorithm: cluster objects into a large number of relatively small sub-clusters
o Use an agglomerative hierarchical clustering algorithm: find the genuine clusters by repeatedly combining these sub-clusters
4 Density-based Method
Clustering based on density (local cluster criterion), such as density-connected points
o Discover clusters of arbitrary shape
o Handle noise
o One scan
o Need density parameters as termination condition
o Eps: Maximum radius of the neighbourhood
o MinPts: Minimum number of points in an Eps-neighbourhood of that point
Typical methods: DBSACN, OPTICS, DenClue
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
DBSCAN: The Algorithm
Arbitrary select a point p
Retrieve all points density-reachable from p w.r.t. Eps and MinPts.
If p is a core point, a cluster is formed.
If p is a border 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.
o OPTICS: Ordering Points To Identify the Clustering Structure
o Produces a special order of the database with its density-based clustering structure
o This cluster-ordering contains info equiv to the density-based clustering’s corresponding to a broad range of parameter settings
o Good for both automatic and interactive cluster analysis, including finding intrinsic clustering structure
o Can be represented graphically or using visualization techniques
DENCLUE: DENsity-based CLUstEring
o Solid mathematical foundation
o Good for data sets with large amounts of noise
o Allows a compact mathematical description of arbitrarily shaped clusters in high-dimensional data sets
o Significant faster than existing algorithm (e.g., DBSCAN)
o But needs a large number of parameters
5. Grid-based Method
Using multi-resolution grid data structure
o The major advantage of this method is fast processing time.
o It is dependent only on the number of cells in each dimension in the quantized space.
o Typical methods: STING, WaveCluster, CLIQUE
o STING: a STatistical INformation Grid approach
o The spatial area area is divided into rectangular cells
o There are several levels of cells corresponding to different levels of resolution
o Each cell at a high level is partitioned into a number of smaller cells in the next lower level
o Statistical info of each cell is calculated and stored beforehand and is used to answer queries
o 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.
o Use a top-down approach to answer spatial data queries
o Start from a pre-selected layer—typically with a small number of cells
o For each cell in the current level compute the confidence interval
· WaveCluster: Clustering by Wavelet Analysis
· A multi-resolution clustering approach which applies wavelet transform to the feature space
· How to apply wavelet transform to find clusters
o Summarizes the data by imposing a multidimensional grid structure onto data space
o These multidimensional spatial data objects are represented in a n-dimensional feature space
o Apply wavelet transform on feature space to find the dense regions in the feature space
o Apply wavelet transform multiple times which result in clusters at different scales from fine to coarse
· Wavelet transform: A signal processing technique that decomposes a signal into different frequency sub-band (can be applied to n-dimensional signals)
· Data are transformed to preserve relative distance between objects at different levels of resolution
· Allows natural clusters to become more distinguishable
6 Model-based methods
· Attempt to optimize the fit between the given data and some mathematical model
· Based on the assumption: Data are generated by a mixture of underlying probability distribution
o In this method a model is hypothesize for each cluster and find the best fit of data to the given model.
o This method also serve a way of automatically determining number of clusters based on standard statistics, taking outlier or noise into account. It therefore yields robust clustering methods.
o Typical methods: EM, SOM, COBWEB
o EM — A popular iterative refinement algorithm
o An extension to k-means
Assign each object to a cluster according to a weight (prob. distribution)
New means are computed based on weighted measures
o General idea
Starts with an initial estimate of the parameter vector
Iteratively rescores the patterns against the mixture density produced by the parameter vector
The rescored patterns are used to update the parameter updates
Patterns belonging to the same cluster, if they are placed by their scores in a particular component
o Algorithm converges fast but may not be in global optima
o COBWEB (Fisher’87)
A popular a simple method of incremental conceptual learning
Creates a hierarchical clustering in the form of a classification tree
Each node refers to a concept and contains a probabilistic description of that concept
o SOM (Soft-Organizing feature Map)
o Competitive learning
o Involves a hierarchical architecture of several units (neurons)
o Neurons compete in a ―winner-takes-all‖ fashion for the object currently being presented
o SOMs, also called topological ordered maps, or Kohonen Self-Organizing Feature Map (KSOMs)
o It maps all the points in a high-dimensional source space into a 2 to 3-d target space, s.t., the distance and proximity relationship (i.e., topology) are preserved as much as possible
o Similar to k-means: cluster centers tend to lie in a low-dimensional manifold in the feature space
o Clustering is 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
o SOMs are believed to resemble processing that can occur in the brain
o Useful for visualizing high-dimensional data in 2- or 3-D space
7 Constraint-based Method
o Clustering by considering user-specified or application-specific constraints
o Typical methods: COD (obstacles), constrained clustering
o Need user feedback: Users know their applications the best
o Less parameters but more user-desired constraints, e.g., an ATM allocation problem: obstacle & desired clusters
o Clustering in applications: desirable to have user-guided (i.e., constrained) cluster analysis
o Different constraints in cluster analysis:
Constraints on individual objects (do selection first)
Cluster on houses worth over $300K o Constraints on distance or similarity functions
Weighted functions, obstacles (e.g., rivers, lakes) o Constraints on the selection of clustering parameters
# of clusters, MinPts, etc. o User-specified constraints
Contain at least 500 valued customers and 5000 ordinary ones o Semi-supervised: giving small training sets as ―constraints‖ or hints
o Example: Locating k delivery centers, each serving at least m valued customers and n ordinary ones
o Proposed approach
Find an initial ―solution‖ by partitioning the data set into k groups and satisfying user-constraints
Iteratively refine the solution by micro-clustering relocation (e.g., moving δ μ-clusters from cluster Ci to Cj) and ―deadlock‖ handling (break the microclusters when necessary)
Efficiency is improved by micro-clustering
o How to handle more complicated constraints?
E.g., having approximately same number of valued customers in each cluster?! — Can you solve it?
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