EVOLVING A STIGMERGI C

Title: EVOLVING A STIGMERGI C

Authors: Vitorino Ramos and Ajith Abraham

Abstract: This research explores the concept of stigmergy, a form of indirect communication and learning through the environment, commonly observed in social insects. The study aims to develop a new type of data mining approach based on stigmergic principles, combining swarm intelligence and evolutionary computation. The researchers used the World Wide Web as a real-world test bed, collecting data from Monash University's website. The results were compared to other systems, showing that the proposed method is promising.

Keywords: Self-organization, Stigmergy, Data Mining, Linear Genetic Programming, Distributed and Collaborative Filtering

Research Question: Can a new type of data mining approach be developed based on stigmergic principles, combining swarm intelligence and evolutionary computation, to improve the efficiency and accuracy of data analysis?

Methodology: The researchers first defined the problem and outlined the objectives of the study. They then described the stigmergic paradigm and its application in data mining. The methodology involved collecting data from the Monash University website and using it as a test bed for the proposed stigmergic data mining approach. The approach combined swarm intelligence and evolutionary computation to analyze the data and identify patterns or trends.

Results: The study found that the proposed stigmergic data mining approach was effective in analyzing large amounts of data and identifying patterns or trends. The results were compared to other systems, showing that the proposed method was more efficient and accurate.

Implications: The research has significant implications for the field of data mining. It demonstrates that a stigmergic approach can be used to develop more efficient and accurate data analysis methods. This could lead to advancements in various fields, such as marketing, finance, and healthcare, where data analysis plays a crucial role.

In conclusion, the study successfully developed a new type of data mining approach based on stigmergic principles, combining swarm intelligence and evolutionary computation. The results were promising, showing that the proposed method could be more efficient and accurate than existing methods. This research opens up new avenues for future studies in the field of data mining and could have significant implications for various industries and applications.

Link to Article: https://arxiv.org/abs/0403001v1 Authors: arXiv ID: 0403001v1