Data Mining Different types of Clustering




Different Types of Clustering

 Datamining Different Types of Clustering

Different Types of Clustering

What is Cluster Analysis ?

  • The objects within a group be similar or different from the objects of the other groups. Cluster analysis is the group's data objects that primarily depend on information found in the data. It defines the objects and their relationships.
  • Cluster analysis is similar to other methods that are used to divide data objects into groups. For example, Clustering can be view as a form of Classification. It constructs labeling of objects with Classification, i.e., new unlabeled objects are allowed a class label using a model developed from objects with known class labels. So that, cluster analysis is defined as unsupervised Classification. If the term classification is used without any ability within data mining, then it typically refers to supervised Classification.
  • The terms segmentation and partitioning are generally used Clustering. The term partitioning is usually used in making relation with techniques that separate graphs into subgraphs and that are not connected to Clustering.
  • Segmentation often introduces the division of data into groups using simple methods.

Different Types of Clustering

  • The group of clusters is referred to as clustering. There are different kinds of Clustering, such as
    • Hierarchical versus Partitional
    • Exclusive versus Overlapping versus Fuzzy
    • Complete versus Partial

Hierarchical versus Partitional

  • The most frequently discussed different features among various types of Clustering is whether the clusters sets are nested or unnested, or in more conventional terminology, partitional or hierarchical. A partitional Clustering is usually a distribution of the set of data objects into non-overlapping subsets (clusters) so that each data object is in precisely one subset.
  • If we allow clusters to have subclusters, then we get a hierarchical Clustering, which group of nested clusters that are organized a tree. Each node in the tree is the association of its subclusters, and the tree roots are the cluster, including all the objects.

Exclusive versus Overlapping versus Fuzzy

  • Fuzzy set is defined as one in which an object is associated with any set with a weight that ranges between 0 and 1. In fuzzy Clustering, set the additional constraint, and the sum of weights for each object must be equal to 1. Probabilistic Clustering systems compute the probability each point belongs to a cluster, and these probabilities must sum to 1.

Complete versus Partial

  • Complete Clustering allocates each object to a cluster, partial Clustering does not. A partial Clustering is that a few objects in a data set may not belong to distinct groups.

Different Types of Clusters

  • Clustering addresses to discover helpful groups of objects (Clusters), where the objectives of the data analysis characterize utility.
  • Types of clusters described here are equally valid for different sorts of data.
    • Well-separated Cluster
    • Prototype-Based cluster
    • Graph-Based Cluster
    • Density-Based Cluster
    • Shared- property or Conceptual Clusters

Well-separated Cluster

  • Well-separated clusters do not require to be spherical but can have any shape. Definition of a cluster is satisfied only when the data contains natural clusters that are quite far from one another.
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Datamining Different Types of Clustering2

Prototype-Based Cluster

  • When the data has definite characteristics, the prototype is usually a medoid that is the most representative point of a cluster.
  • Data with continuous characteristics, the prototype of a cluster is usually a centroid.
  • For some sorts of data, the model can be viewed as the most central point, and in such examples, we commonly refer to prototype-based clusters as center-based clusters. As anyone might expect, such clusters tend to be spherical.
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Center Based Clusters

Graph-Based Cluster

  • It is a group of objects that are associated with each other, but that has no association with objects that is outside the group.
  • Other kinds of graph-based clusters also possible. One such way describes a cluster as a clique. It is like prototype-based clusters, and such clusters tend to be spherical.
  • Clique is a set of nodes in a graph that is completely associated with each other.
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Contiguity Based Clusters

Density-Based Cluster

  • A cluster is usually occupied when the clusters are irregularly and intertwined, and when noise and outliers exist. Contiguity-based definition of a cluster would not work properly for the data. Since the noise would tend to form a network between clusters.
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Density Based Clusters

Shared-property or Conceptual Clusters

  • A center-based cluster shares property that they closest to the similar centroid or medoid. the shared-property approach additionally incorporates new types of the cluster.
  • Consider the cluster given in the figure. A triangular area (cluster) is next to a rectangular one, and there are two intertwined circles (clusters). In both cases, a Clustering algorithm would require a specific concept of a cluster to recognize these clusters effectively. The way of discovering such clusters is called conceptual Clustering.
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Conceptual Clusters



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