PostHeaderIcon Discovery-based Learning: Clustering

Discovery is a restricted form of learning. The knowledge acquisition is done without getting any assistance from a teacher. Discovery Learning is an inquiry-based learning method.

In discovery learning, the learner uses his own experience and prior knowledge to discover the truths that are to be learned. The learner constructs his own knowledge by experimenting with a domain, and inferring rules from the results of these experiments. In addition to domain information the learner need some support in choosing and interpreting the information to build his knowledge base.

A cluster is a collection of objects which are similar in some way. Clustering groups data items into similarity classes. The properties of these classes can then be used to understand problem characteristics or to find similar groups of data items. Clustering can be defined as the process of reducing a large set of unlabeled data to manageable piles consisting of similar items. The similarity measures depend on the assumptions and desired usage one brings to the data.

Clustering begins by doing feature extraction on data items and measure the values of the chosen feature set. Then the clustering model selects and compares two sets of data items and outputs the similarity measure between them. Clustering algorithms that use particular similarity measures as subroutines are employed to produce clusters.

The clustering algorithms are generally classified as Exclusive Clustering, Overlapping Clustering, Hierarchical Clustering and Probabilistic Clustering. The selection of clustering algorithms depends on various criteria such as time and space complexity. The results are checked to see if they meet the standard otherwise some or all of the above steps have to be repeated.

Some of the applications of clustering are data compression, hypothesis generation and hypothesis testing. The conceptual clustering system accepts a set of object descriptions in the form of events, observations, facts and then produces a classification scheme over the observations.

COBWEB is an incremental conceptual clustering system. It incrementally adds the objects into a classification tree. The attractive feature of incremental systems is that the knowledge is updated with each new observation. In COBWEB system, learning is incremental and the knowledge it learned in the form of classification trees increase the inference abilities.

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