agent based clustering | Ricerc@Sapienza

Data mining by evolving agents for clusters discovery and metric learning

In this paper we propose a novel evolutive agent-based clustering algorithm where agents act as individuals of an evolving population, each one performing a random walk on a different subset of patterns drawn from the entire dataset. Such agents are orchestrated by means of a customised genetic algorithm and are able to perform simultaneously clustering and feature selection.

A supervised classification system based on evolutive multi-agent clustering for smart grids faults prediction

Due to the increasing amount of sensors and data streams that can be collected in order to monitor electric distribution networks, developing predictive diagnostic systems over Smart Grids demands powerful and scalable algorithms in order to search for regularities in Big Data. In this regards, Evolutive Agent Based Clustering (E-ABC) is a promising framing reference, as it is conceived to orchestrate a swarm of intelligent agents acting as individuals of an evolving population, each performing a random walk on a different subset of patterns.

An evolutionary agents based system for data mining and local metric learning

Discovering regularities in Big Data is nowadays a crucial task in many different applications, from bioinformatics to cybersecurity. To this aim, a promising approach consists in performing data clustering with Local Metric Learning, i.e. trying to discover well-formed (compact and populated) clusters and, at the same time, a suitable subset of features corresponding to the subspace where each cluster lies.