Co-Clustering Can Provide Industrial Data Pattern Discovery

In spite of the rapid development in data acquisition technology resulting in the explosive collection of acquired datasets, techniques such as data organization and classification, manipulation, and analysis of very large, diverse, heterogeneous datasets have only evolved modestly. This has led to hindrances in effective utility and better understanding of the acquired, large-scale data for knowledge discovery. In an industrial setting, an interesting visual from McKinsey illustrates that despite collecting data from tens of thousands of sensors, less than 1% is actually utilized. Data clustering is the classification of data objects into different groups (clusters) such that data objects in one group are similar together and dissimilar from another group. Typically, homogeneous data objects, i.e. data objects having the same data type, are grouped together using some of the well-known clustering algorithms.