Special Session on Applications of Nature-inspired Computing Algorithms in Data Mining

Data Mining (DM) is a critical phase in the process of Knowledge Discovery in Databases (KDD). This process generally consists of the following three phases: Pre-processing, Data Mining and Post-processing. It is important in modern days as the world is sitting on a heap of data with an intense desire to obtain the knowledge contained in it and the KDD involves applying specific algorithms for automatically discovering high level knowledge from real-world, large and complex datasets. Hence, researchers from both the academia and the industry are attracted in this area because of its application in various domains and the challenges.

Nature-inspired algorithms are the latest state-of-the-art algorithms which are designed observing natural evolution process or other natural phenomenon, e.g., social behavior of birds, insects. These methods aim to exploit the tolerance for imprecision, uncertainty, approximate reasoning, and partial truth in order to achieve tractability, robustness and low-cost solutions. Interestingly, it suits the data mining community very well as the modern datasets ate very large, complex and dynamic. The size and complexity of the problems nowadays require the development of methods and solutions whose efficiency is measured by their ability to find acceptable results within a reasonable amount of time, rather than an ability to guarantee the optimal solution.

The aim of the session is to provide a forum to disseminate and discuss Nature-inspired algorithms in data mining, e.g., pattern analysis, association, correlation, classification, prediction, clustering, in data preprocessing, e.g., data cleaning, data integration, data transformation, data reduction, and in handling large-size datasets. The real-world applications and identification of new promising research directions in this area would be an additional opportunity.

Topics include, but are not restricted to:

1.      Data Mining using Nature-inspired algorithms, e.g., Evolutionary, Swarm, Fuzzy, Neural.

2.      Data Mining using hybrid algorithms, e.g., Neuro-Fuzzy, Neuro-GA, Fuzzy-GA.

3.      Data Preprocessing, e.g., feature selection, data reduction, and Nature-inspired algorithms.

4.      Mining large-size datasets using Nature-inspired algorithms.

5.      Solving real-world data mining applications using Nature-inspired algorithms.

6.      Software for Data Mining and Nature-inspired algorithms

Paper Submission

Please follow the instructions given at the corresponding section.

Pramod Kumar Singh: 
pksingh@iiitm.ac.in | pksingh7@gmail.com

ABV - Indian Institute of Information Technology and Management Gwalior, India.