Systematic Review: A State of Art ML Based Clustering Algorithms for Data Mining

Data mining is an unsupervised learning technique to extract the insights and hidden relationships among data. Data mining has more importance in data science and machine learning because through data mining all hidden information is shown to determine various aspects of the data set. Clustering is a data mining technique to group the data, on the basis of similarity measures. The objects or data points in a cluster are similar. Similarly, objects or data points in another cluster will also be similar. But when these clusters are compared, they are dissimilar to each other. Clustering is considered the most important unsupervised learning technique because it deals with finding a structure in a collection of unlabeled data. Clustering can be done by the different approaches like partitioning clustering, hierarchical clustering, density-based clustering, and grid-based clustering. These clustering approaches can be done by the numbers of algorithms, such as K-means clustering, Fuzzy C-means clustering, Hierarchical clustering, DBSCAN, OPTICS, STING, ROCK and CACTUS. This proposed paper contained reviews of the above techniques by using a more powerful programming language (Python) as a tool. Evidence attainable from this study is helpful for researchers to select an appropriate clustering approach based on their domain.