Editorial survey: swarm intelligence for data mining

Abstract

This paper surveys the intersection of two fascinating and increasingly popular domains: swarm intelligence and data mining. Whereas data mining has been a popular academic topic for decades, swarm intelligence is a relatively new subfield of artificial intelligence which studies the emergent collective intelligence of groups of simple agents. It is based on social behavior that can be observed in nature, such as ant colonies, flocks of birds, fish schools and bee hives, where a number of individuals with limited capabilities are able to come to intelligent solutions for complex problems. In recent years the swarm intelligence paradigm has received widespread attention in research, mainly as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). These are also the most popular swarm intelligence metaheuristics for data mining. In addition to an overview of these nature inspired computing methodologies, we discuss popular data mining techniques based on these principles and schematically list the main differences in our literature tables. Further, we provide a unifying framework that categorizes the swarm intelligence based data mining algorithms into two approaches: effective search and data organizing. Finally, we list interesting issues for future research, hereby identifying methodological gaps in current research as well as mapping opportunities provided by swarm intelligence to current challenges within data mining research.Editor: Foster Provost.

Swarm Intelligence in Big Data Analytics

“…Generally, there are two kinds of approaches that apply swarm intelligence as data mining techniques [27]. The first category consists of techniques where individuals of a swarm move through a solution space and search for solution(s) for the data mining task.…”

ACO-Based Bayesian Network Ensembles for the Hierarchical Classification of Ageing-Related Proteins

“…The main idea is to utilize a swarm of simple individuals that use collective behaviour to achieve a certain goal. ACO algorithms have been successful in solving several combinatorial optimization problems, including classification rules discovery [12,11] and general purpose BN construction [2,13,23]. However, ABC-Miner [14], recently introduced by the authors, is the first ACO algorithm to learn BN classifiers.…”

A study of different quality evaluation functions in the cAnt-Miner(PB) classification algorithm

“…Several extensions of Ant-Miner have been proposed in the literature, as reviewed in [8]. The majority of these extensions maintain the overall structure of the algorithm-i.e., the algorithm employs an ACO-based procedure to create individual rules in order to produce a complete classification model (list of rules).…”

“…The algorithm finishes once the training set has less than a predefined number of training examples remaining. Most of the proposed extensions of Ant-Miner follow this same strategy to create a list of rules [8].…”