These articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.
Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.
Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.
International Journal of Data Science (3 papers in press)
Mining Recent Frequent Co-occurring Items from Transaction Stream by Sumit Misra, Soumyadeep Thakur, Manosij Ghosh, Sanjoy Kumar Saha Abstract: The work presents a simple but novel scheme to determine the recent frequent co-occurring items from transaction streams. It enables to follow the current trend and pattern of occurrence of the items. Co occurrence of items can also help in recommendation. Proposed methodology works on a sequence of transaction forming a time-window. Based on the items present in a transaction, item-sets are formed and encoded. Item-sets denote different subset of items in the transaction. An in-memory structure maintains the frequency of the item-sets. Frequency is time-decayed count that prioritizes the recent occurrences. In case the in-memory structure grows to exceed the allowable memory then a pruning operation is carried out to remove the less frequent item-sets. Threshold for pruning is chosen dynamically. Processing is done at the end of the window and it reduces the computational burden. The work also analyzes the impact of the decaying parameter and window size. Experiment has been carried out on two datasets and performance has been compared with two recent works. It is observed that proposed methodology provides a better accuracy. Furthermore, low time and space complexity make the methodology suitable for stream analytic. Keywords: Recent Frequent Item-set; Time Decaying Count; Item Pruning; Stream Analytic.
Multi-level Fuzzy Overlapping Community Detection Algorithm for Weighted Networks by Ali Choumane, Ali Harkous Abstract: To understand the hierarchical structure of large networks we propose a multi-level community detection algorithm. Each level consists of a set of fuzzy overlapping communities where a node can belong to multiple communities associated with belonging membership degrees. Our algorithm works for weighted and unweighted networks as well. For each level of hierarchy, it identifies the centres of potential communities and iteratively expands each of them to form the final communities. A new vector representation of the nodes is proposed and used in the centre's expansion process and in the computation of the belonging degrees. Communities detected at a given level serve as super nodes while identifying the overlapping communities of a higher level. Experiments achieved on the well-known LFR benchmark showed high performance which is measured by the normalized mutual information (NMI). Unlike existing methods, our algorithm shows good and stable performance when varying the number of communities of the overlapping nodes (2 to 8 communities for each overlapping node). Keywords: Network analysis; community detection; overlapping communities; fuzzy overlapping.
Reversion and Location Trends in the Bitcoin Market by Guoyi Zhang Abstract: The cryptocurrency market is different from traditional markets due to its unique property, which allows trading around the clock year round and globally. It is of interest to investigate if the traditional stock market techniques can be applied to cryptocurrency markets. In this research, we studied application of the 75 percent reversion rule in cryptocurrency markets. We also identified active geographic locations/markets at certain time and examined government regulations and news\' influence on the cryptocurrency markets. This can help investors and institutions make decisions before the next geographic location comes active, and help researchers understand how news can affect the market within each region and how it spreads globally. Keywords: Bitcoin; cryptocurrency market; 75% reversion rule; local linear regression; geographic location identification.