In this paper we propose a novel algorithm for mining contrast patterns using a distributed, map-reduce like framework. Contrast patterns describe differences between contrasted data sets and have previously been used for building highly accurate classifiers. However, mining for contrast patterns is a computationally expensive task and existing algorithms are designed to run in a sequential manner on a single machine. Consequently, existing approaches are unable to handle dense, high volume and high dimensional databases. Our algorithm addresses this problem by partitioning the search-space for contrast patterns into small, independent units. These units can be mined in parallel, providing a scalable solution for mining large data sets. Using three different real-world data sets we test an implementation of our algorithm on a Spark cluster. Results of these tests indicate that our algorithm achieves a high-degree of parallelism and scalability.

This paper revisits a traditional Ant Foraging algorithm and proposes a Cluster-based Softbots algorithm to address the performance issues caused by constraints of random autonomous search featured in most swarm intelligence-based algorithms. A simple hierarchy is introduced to regulate the unfolding of dynamically changing swarm-like behaviors. Comparative experiments for Ant Foraging and the proposed Cluster-based Softbots are described. The results demonstrate that Softbots have significant comparative advantages over a traditional Ant Foraging algorithm on the benchmark criteria in the presented experimental settings. It is shown that Softbots are more suitable for resource-lean search circumstances whereas not many individual agents can be allocated.