BDM19

Updates

Paper submission deadline: January 31st, 2019

Papers from the workshop will be published in an LNCS/LNAI post Proceedings of PAKDD Workshops published by Springer

Submission Link: link-to-submission

PAKDD 8TH workshop on “Biologically Inspired Techniques for Data Mining (BDM’19)”, is to be held in conjunction with 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD’19 ), April – 14th, 2019, Macau, China. PAKDD is one of the major conferences in knowledge discovery and data mining.

Introduction

For the last few years, biologically inspired data mining techniques have been intensively used in different data mining applications such as data clustering, classification, association rule mining, sequential pattern mining, outlier detection, feature selection and bioinformatics. The techniques include Neural Networks, Evolutionary Computation, Fuzzy Systems, Genetic Algorithms, Ant Colony Optimization, Particle Swarm Optimization, Artificial Immune Systems, Culture Algorithms, Social evolution, and Artificial Bee Colony Optimization. A huge increase in the number of papers published in the area has been observed in the last decade. Most of these techniques use optimization to speed up the data mining process and improve the quality of patterns mined from the data. The aim of the workshop is to highlight the current research related to biologically inspired techniques in different data mining domains and their implementation in real life data mining problems. The workshop will provide a platform to the researcher from computational intelligence and evolutionary computation and other biologically inspired techniques to get feedback on their work from other data mining perspective such as statistical data mining, AI and machine learning based data mining.

The workshop highlights a relatively new but fast-growing area of data mining which is based on optimization techniques from the biological behavior of animals, insects, cultures, social behaviors and biological evolution. Techniques based on these models have been studied substantially, well optimized and tuned for different application areas in the previous decade. Knowledge Discovery and Data mining have been observed as one of the fastest growing application areas of these nature-inspired techniques.

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