Co-clustering of Fuzzy Lagged Data
  The paper focuses on mining patterns that are characterized by a fuzzy lagged
relationship between the data objects forming them. Such a regulatory mechanism
is quite common in real life settings. It appears in a variety of fields:
finance, gene expression, neuroscience, crowds and collective movements are but
a limited list of examples. Mining such patterns 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 fuzzy lagged co-cluster is an NP-complete problem.
We thus present a polynomial-time Monte-Carlo approximation algorithm for
mining fuzzy lagged co-clusters. We prove that for any data matrix, the
algorithm mines a fuzzy lagged co-cluster with fixed probability, which
encompasses the optimal fuzzy lagged co-cluster by a maximum 2 ratio columns
overhead and completely no rows overhead. Moreover, the algorithm handles
noise, anti-correlations, missing values and overlapping patterns. The
algorithm was extensively evaluated using both artificial and real datasets.
The results not only corroborate the ability of the algorithm to efficiently
mine relevant and accurate fuzzy lagged co-clusters, but also illustrate the
importance of including the fuzziness in the lagged-pattern model.
