To understand the role of unsupervised learning, it is important to first look at the overall life cycle of the data-mining process. There are different methodologies that divide the life cycle of the data-mining process into different independent stages, called phases. Currently, there are two popular ways to represent the data-mining life cycle:
CRISP-DM (Cross-Industry Standard Process for Data Mining) life cycle
SEMMA (Sample, Explore, Modify, Model, Access) data-mining process
CRISP-DM was developed by a consortium of data miners who belonged to various companies, including Chrysler and SPSS (Statistical Package for Social Science). SEMMA was proposed by SAS (Statistical Analysis ...
