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Framework of EcomTDMA for Transactional Data Mining Using Frequent Item Set for E-Commerce Application

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Data Management, Analytics and Innovation

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 71))

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Abstract

In data mining, comprehending out the common item set is an indispensable job. In statements such as participation rule mining and co-relationships, these conventional item sets are beneficial. These systems use particular algorithms to secure numerous item sets, but during unnecessary data come opposite; these are unproductive in communicating and coordinating the load. With these algorithms, automated parallelization is not feasible either. There is an inadequacy to build an algorithm to determine these difficulties with modern algorithms that will help the lacking characteristics, such as automatic parallelization, balancing and fair data configuration. We are using a new technique in this paper to discover frequent item sets by using MapReduce. With the HDFS framework, the modified Apriori algorithm is used, which is called the EcomTDMA technique. By using the disintegrate method, the MapReduce approach can manipulate individually and simultaneously in this system. The consequence of this approach to the reducers and the redactors will show the produce. Three various algorithms like base Apriori, FP-growth and our advanced development Apriori have practiced in the experiment, and the process was performed in both standalone computer and assigned environment and showed the consequences how the recommended algorithm is more substantial than standard algorithms.

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Author information

Authors and Affiliations

  1. Neville Wadia Institute of Management Studies and Research, Pune, 411001, India

    Pradeep Ambavane

  2. AISSM’s Institute of Information Technology, Pune, 411001, India

    Sarika Zaware

  3. RIIM—The Academy School of Business Management, Pune, 411033, India

    Nitin Zaware

Authors
  1. Pradeep Ambavane
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  2. Sarika Zaware
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  3. Nitin Zaware
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Editor information

Editors and Affiliations

  1. Analytics and Insights, Tata Consultancy Services, Pune, Maharashtra, India

    Neha Sharma

  2. A. K. Choudhury School of Information Technology, Kolkata, West Bengal, India

    Amlan Chakrabarti

  3. Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  4. Faculty of Computer Science, Technion—Israel Institute of Technology, Haifa, Israel

    Alfred M. Bruckstein

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Ambavane, P., Zaware, S., Zaware, N. (2022). Framework of EcomTDMA for Transactional Data Mining Using Frequent Item Set for E-Commerce Application. In: Sharma, N., Chakrabarti, A., Balas, V.E., Bruckstein, A.M. (eds) Data Management, Analytics and Innovation. Lecture Notes on Data Engineering and Communications Technologies, vol 71. Springer, Singapore. https://doi.org/10.1007/978-981-16-2937-2_21

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