Clustering and classification methods using unsupervised, semi-supervised and supervised algorithms

Statistical learning, machine learning, and data mining refer to research areas which can all be thought of as products of multivariate statistics. Their common themes are analysis and interpretation of data, often involving large quantities of data, and even more often resorting to numerical methods. These are techniques for interpreting data by comparing them to models for data behaviour which includes nonparametric models and inferential techniques with the fundamental goal of providing insights to natural occurrences. In typical applications, data types are so heterogeneous and diverse that the fundamental methods used for a multidimensional data type may not be effective. Therefore, more emphasis need to be placed on the robust learning algorithms in the context of these different data types. In this research, we propose the application of data mining techniques via clustering and classification methods using unsupervised, semi-supervised and supervised learning algorithms for large data set, which will provide the basis for higher classification accuracy and prediction of complex space and time observations.