This book presents the abstracts of the papers presented to the Online National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020 (RDMPMC-2020) held on 26th and 27th August 2020 organized by the Department of Metallurgical and Materials Science in Association with the Department of Production and Industrial Engineering, National Institute of Technology Jamshedpur, Jharkhand, India.
For analysis of transaction data, Frequent Itemset mining is important task in data mining. In literature work lot of work has been done on frequent Itemset mining task. But this work focuses on static dataset. Whole dataset is used for Itemset mining. The proposed work focuses on temporal data. Temporal data includes timestamp information. The dataset is sliced in cubes for analysis. The Apriori algorithm is used for Itemset mining. This data cube information provide periodic or time interval specific frequent Itemset. To define time cubes data density is defined to avoid overestimating the time period. To improve the system performance the system is implemented in multi-threaded environment. The performance of system will measured in terms of time and memory.
A variety of approaches to the intelligent analysis of complex manufacturing data have been described in the literature. A comparative analysis of these systems reveals underlying similarities in their functional organizations. This leads to the development of a general functional model for intelligent data analysis (IDA).This article describes the various data analysis systems studied, the general model for IDA, and the implementation of a demonstration IDA system. The implementation serves to demonstrate the feasibility of this approach, and provides directions for further research.
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