A Metaheuristic Approach for Mining Gradual Patterns

Swarm intelligence is a discipline that studies the collective behavior that is produced by local interactions of a group of individuals with each other and with their environment. In Computer Science domain, numerous swarm intelligence techniques are applied to optimization problems that seek to efficiently find best solutions within a search space. Gradual pattern mining is another Computer Science field that could benefit from the efficiency of swarm based optimization techniques in the task of finding gradual patterns from a huge search space. A gradual pattern is a rule-based correlation that describes the gradual relationship among the attributes of a data set. For example, given attributes {G,H} of a data set a gradual pattern may take the form: "the less G, the more H". In this paper, we propose a numeric encoding for gradual pattern candidates that we use to define an effective search space. In addition, we present a systematic study of several meta-heuristic optimization techniques as efficient solutions to the problem of finding gradual patterns using our search space.

Ant Colony Optimization for Mining Gradual Patterns

Gradual pattern extraction is a field in (KDD) Knowledge Discovery in Databases that maps correlations between attributes of a data set as gradual dependencies. A gradual dependency may take a form of "the more Attribute K , the less Attribute L". In this paper, we propose an ant colony optimization technique that uses a probabilistic approach to learn and extract frequent gradual patterns. Through computational experiments on real-world data sets, we compared the performance of our ant-based algorithm to an existing gradual item set extraction algorithm and we found out that our algorithm outperforms the later especially when dealing with large data sets.

Capturing the temporal constraints of gradual patterns

Gradual pattern mining allows for extraction of attribute correlations through gradual rules such as: "the more X, the more Y". Such correlations are useful in identifying and isolating relationships among the attributes that may not be obvious through quick scans on a data set. For instance, a researcher may apply gradual pattern mining to determine which attributes of a data set exhibit unfamiliar correlations in order to isolate them for deeper exploration or analysis. In this work, we propose an ant colony optimization technique which uses a popular probabilistic approach that mimics the behavior biological ants as they search for the shortest path to find food in order to solve combinatorial problems. In our second contribution, we extend an existing gradual pattern mining technique to allow for extraction of gradual patterns together with an approximated temporal lag between the affected gradual item sets. Such a pattern is referred to as a fuzzy-temporal gradual pattern and it may take the form: "the more X, the more Y, almost 3 months later". In our third contribution, we propose a data crossing model that allows for integration of mostly gradual pattern mining algorithm implementations into a Cloud platform. This contribution is motivated by the proliferation of IoT applications in almost every area of our society and this comes with provision of large-scale time-series data from different sources.