Discovery of Temporal Neighborhoods through Discretization Methods and Markov Model

This article describes a few approaches and algorithms for temporal neighborhood discretization from a couple of papers accepted and published in the conference ICDM (2009) and in the journal IDA (2014), authored by me and my fellow professors Dr. Aryya Gangopadhyay and Dr. Vandana Janeja at UMBC. Almost all of the texts / figures in this blog post are taken from the papers. Neighborhood discovery is a precursor to knowledge discovery in complex and large datasets such as Temporal data, which is a sequence of data tuples measured at successive time instances. Hence instead of mining the entire data, we are interested in dividing the huge data into several smaller intervals of interest which we call as temporal neighborhoods. All the algorithms start by dividing a given time series data into a few (user-chosen) initial number of bins (with equal-frequency binning) and then they proceed merging similar bins (resulting a few possibly unequal-width bins) using different approaches, finally the algorithms terminate if we obtain the user-specified number of output (merged) bins .