Co-clustering of Lagged Data
The paper focuses on mining clusters that are characterized by a lagged relationship between the data objects. We call such clusters lagged co-clusters. A lagged co-cluster of a matrix is a sub matrix determined by a subset of rows and their corresponding lag over a subset of columns. Extracting such subsets (not necessarily successive) may reveal an underlying governing regulatory mechanism. Such a regulatory mechanism is quite common in real life settings. It appears in a variety of fields: meteorology, seismic activity, stock market behavior, neuronal brain activity, river flow and navigation, are but a limited list of examples. Mining such lagged co-clusters not only helps in understanding the relationship between objects in the domain, but assists in forecasting their future behavior. For most interesting variants of this problem, finding an optimal lagged co-cluster is an NP-complete problem. We present a polynomial-time Monte-Carlo algorithm for finding a set of lagged co-clusters whose error does not exceed a pre-specified value, which handles noise, anti-correlations, missing values, and overlapping patterns. Moreover, we prove that the list includes, with fixed probability, a lagged co-cluster which is optimal in its dimensions. The algorithm was extensively evaluated using various environments. First, artificial data, enabling the evaluation of specific, isolated properties of the algorithm. Secondly, real-world data, using river flow and topographic data, enabling the evaluation of the algorithm to efficiently mine relevant and coherent lagged co-clusters in environments that are temporal, i.e., time reading data, and non-temporal, respectively.