抽象的

Performance Evaluation of Learning by Example Techniques over Different Datasets

D.Ramya , D.T.V.Dharmajee Rao

The clustering activity is an unsupervised learning observation which coalesce the data into segments. Grouping of data is done by identifying common characteristics that are labeled as similarities among data based on their characteristics. Scheming the Performance of selective clustering algorithms over different chosen data sets are evaluated here. Burst time is a performance parameter chosen in evaluating the performance of various selective clustering based machine learning algorithms. Here the investigational results are represented in a table. In our investigation we also suggest a clustering algorithm that performs quicker over a selected data set with reference to the parameter Burst time