Adaptive Threshold Based Clustering: A Deterministic Partitioning Approach

2. Literature Review

In 1960s, analysts utilized “Data Fishing” or Data Digging” for getting information from the repositories. They developed numerous methods to parcel it into various groups where objects in each group is similar to one another. For this purpose, MacQueen (1967) developed k-means method for acquiring k clusters, where each cluster is separated from other cluster based on the distance from the centroid and data objects. From that point onward, Salton and Wong (1971) acquainted single pass clustering for parcel it into disjoint sets. Duda and Hart (1973) portrayed how parceling the datasets is important in pattern recognition and acknowledgment. Dubes and Jain (1976) emphasized that the user should be aware of intrinsic properties of a clustering technique before using them. Lloyd (1982) presented k-means method for apportioning the datasets. In this, separation between the data objects and centroids is least as they are at the minimum distance. On the other hand, in 1987, Kaufman presented k-medoid method, depends on the hunt of k delegates called medoids for the provided databases.