Map-Reduce (Hadoop) based Data Clustering for BigData: A Survey

Data clustering problems are challenging due to bigdata. The volume, value, variety, velocity, veracity and structure of data makes clustering more complex. Processing time for data mining is directly proportional to the data to be processed. To access big data efficiently, data clustering may be a good solution. Traditional data clustering algorithms have been considered as NP-Hard problem. The clustering of big data also comes under the roof of the NP-Hard problem. Clustering algorithms require huge computations to make the cluster of data. An increase in data increases the number of computations. Hence, research in use of distributed computing for clustering algorithms and improvising resources utilization. However, distributed computing gives the worst results when data has dependencies. Distributed system like Hadoop run the clustering algorithms as the same code on partitioned data on different nodes of computing and finally gathers the result in one place. This paper gives a survey of Map-Reduce based big data clustering algorithms and problem solved.