Abstract
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. In this paper we propose a class of algorithms to generate temporal neighborhoods through unequal depth discretization.
We describe four novel algorithms (a) Similarity based Merging (SMerg), (b) Stationary distribution based Merging (StMerg), (c) Greedy Merge (GMerg) and, (d) Optimal Merging (OptMerg). The SMerg and STMerg algorithms are based on the robust framework of Markov models and the Markov Stationary distribution respectively. GMerg is a greedy approach and OptMerg algorithm is geared towards discovering optimal binning strategies for the most effective partitioning of the data into temporal neighborhoods. Both these algorithms do not use Markov models. We identify temporal neighborhoods with distinct demarcations based on unequal depth discretization of the data. We discuss detailed experimental results in both synthetic and real world data. Specifically, we show (i) the efficacy of our algorithms through precision and recall of labeled bins, (ii) the ground truth validation in real world traffic monitoring datasets and, (iii) Knowledge discovery in the temporal neighborhoods such as global anomalies. Our results indicate that we are able to identify valuable knowledge based on our ground truth validation from real world traffic data.