Abstract
Clustering is a very useful scheme for data structuring and retrieval behuhcause it can handle large volumes of multi-dimensional data and employs a very fast algorithm. Other forms of data structuring techniques include hashing and binary tree structures. However, clustering has the advantage of employing little computational storage requirements and a fast speed algorithm. In this paper, clustering, k-means clustering and the approaches to effective clustering are extensively discussed. Clustering was employed as a data grouping and retrieval strategy in the filtering of fingerprints in the Fingerprint Verification Competition 2000 database 4(a). An average penetration of 7.41% obtained from the experiment shows clearly that the clustering scheme is an effective retrieval strategy for the filtering of fingerprints.
Highlights
A collection of datasets may be too large to handle and work on may be better grouped according to some data structure
Clustering is a useful and efficient data structuring technique because it can handle datasets that are very large and at the same time n-dimensional and similar datasets are assigned to the same clusters [9]
Clustering is a process of organizing a collection of data into groups whose members are similar in some way [9, 10, 11, 12] According to Jain et al [13] “Cluster analysis is the organization of a collection of patterns into clusters based on similarity”
Summary
A collection of datasets may be too large to handle and work on may be better grouped according to some data structure. Clustering is a useful and efficient data structuring technique because it can handle datasets that are very large and at the same time n-dimensional (more than 2 dimensions) and similar datasets are assigned to the same clusters [9]. Clustering is a process of organizing a collection of data into groups whose members are similar in some way [9, 10, 11, 12] According to Jain et al [13] “Cluster analysis is the organization of a collection of patterns (usually represented as a vector of measurements, or a point in a multidimensional space) into clusters based on similarity”. A similarity measure is used for the assignment of patterns or features to clusters
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