Int. J. Business Intelligence and Data Mining, Vol. 11, No. 2, 2016 151 Copyright © 2016 Inderscience Enterprises Ltd. Automated support thresholds for rule mining Chenniangirivalasu Sadhasivam Kanimozhi Selvi*, Subramaniam Malliga and Shanmuga Vadivel Kogilavani Department of Computer Science and Engineering, Kongu Engineering College, Erode, Tamil Nadu, India Email: kanimozhiselvics0674@gmail.com Email: s.vkogilavani@yahoo.com Email: smalliga14@yahoo.com *Corresponding author Abstract: Association rule mining is an important task in data mining which discovers hidden associations between items in the database based on user-specified support and confidence thresholds. To find the relevant associations, an appropriate threshold has to be specified. The support threshold plays a vital role in the quantity and quality of the rules found. The challenge is that one should not miss the rare associations and on the other hand uninteresting associations should not be generated. This paper proposes an approach to obtain the appropriate support thresholds at each level of the level-wise mining approach. It sets the support threshold by analysing the frequency of items and their associations in the database at each level. It uses the central measure of tendency and measure of dispersion to analyse the database and sets the thresholds accordingly. The performance of the proposed approach has been evaluated against multiple sparse and dense datasets. Experimental results show that this approach produces the interesting rules without specifying the user specified support threshold. Keywords: measure of central dispersion; association rules; adaptive support; apriori; automated support; frequent items; support distribution. Reference to this paper should be made as follows: Selvi, C.S.K., Malliga, S. and Kogilavani, S.V. (2016) ‘Automated support thresholds for rule mining’, Int. J. Business Intelligence and Data Mining, Vol. 11, No. 2, pp.151–170. Biographical notes: Chenniangirivalasu Sadhasivam Kanimozhi Selvi is an Associate Professor at the Department of Computer Science and Engineering at Kongu Engineering College, Erode, Tamil Nadu, India. She is interested in data mining and has received her PhD in Association Rule Mining from Anna University, Chennai. Her current research interests are big data analytics and data mining. Her research articles are published in national and international journals. She has involved herself in big data analytics. Currently, she is guiding four research scholars. She has also guided many UG and PG projects. She has published 20 articles in international journals and presented more than 30 papers in national and international conferences in her research and other technical areas. She is also interested in cloud and big data analytics.