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Knowledge Discovery and Data Mining for Intelligent Business Solutions

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Advances in Data and Information Sciences

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 318))

Abstract

Data Mining refers to the process of extracting previously unknown and useful patterns from large datasets. Traditional data mining approaches focus mainly on finding the most frequent patterns from databases. However, mining the frequent patterns alone is not sufficient in all scenarios. For example, a business manager might be more interested to find the most profitable items by taking into account frequency and profit both, rather than mining the most common items alone. Therefore, in recent years, the research focus has shifted to mining of high utility patterns from datasets, where utility is used to represent users’ preference and it can be cost, profit or any other aesthetic value depending upon the application. High-utility itemset mining (HUIM) deals with the problem of finding high utility itemsets (HUIs), where every item is associated with atleast two utility values-internal and external. HUIM has found numerous applications in web mining, cross-marketing, customer segmentation, medical treatments, etc. This paper explains the concept of HUIM, it’s relevance and applications, and also provides an in depth analysis of techniques and advancements in the field of HUIM.

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Author information

Authors and Affiliations

  1. Jawaharlal Nehru University, New Delhi, New Delhi, India

    Pushp & Satish Chand

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  1. Pushp
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  2. Satish Chand
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Editor information

Editors and Affiliations

  1. Krishna Engineering College, Ghaziabad, India

    Shailesh Tiwari

  2. National Institute of Technology Agartala, Agartala, Tripura, India

    Munesh C. Trivedi

  3. Department of Engineering and Science, University of Agder, Kristiansand, Norway

    Mohan Lal Kolhe

  4. Motilal Nehru National Institute of Technology, Allahabad, Uttar Pradesh, India

    K.K. Mishra

  5. Department of Computer Science and Engineering, RBS Engineering Technical Campus, Agra, Uttar Pradesh, India

    Brajesh Kumar Singh

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Pushp, Chand, S. (2022). Knowledge Discovery and Data Mining for Intelligent Business Solutions. In: Tiwari, S., Trivedi, M.C., Kolhe, M.L., Mishra, K., Singh, B.K. (eds) Advances in Data and Information Sciences. Lecture Notes in Networks and Systems, vol 318. Springer, Singapore. https://doi.org/10.1007/978-981-16-5689-7_18

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