A Review on Unstructured Data using k-Mean Algorithm
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.
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