Open AccessA Review on Unstructured Data using k-Mean Algorithm
Author(s) -
Akhilesh Sharma
Publication year - 2020
Publication title -
smart moves journal ijoscience
Language(s) - English
Resource type - Journals
ISSN - 2582-4600
DOI - 10.24113/ijoscience.v6i6.290
Subject(s) - cluster analysis , computer science , partition (number theory) , cluster (spacecraft) , unstructured data , knowledge extraction , cure data clustering algorithm , data mining , canopy clustering algorithm , k means clustering , pattern recognition (psychology) , data stream clustering , correlation clustering , algorithm , artificial intelligence , big data , mathematics , combinatorics , programming language
Unstructured data are the data without identifiable structure, audio, video and images are few examples. Clustering one of the best techniques in the knowledge extraction process. It is nothing but a grouping of similar data to form a cluster. The distance between the data in one cluster and the other should not be less. Many algorithms are practiced for clustering, in that k-mean clustering is one of the popular terms for cluster analysis. The main aim of the algorithm is to partition the dataset into k clusters based on some computational value. The limitation of k-mean clustering is that it can be applied to either structured or unstructured, not in combination with both. This paper overcomes that limitation by proposing a new k –mean algorithm for extracting hidden knowledge by forming clusters from the combination of unstructured datasets.