Knowledge Discovery and Data Mining for Intelligent Business Solutions

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 item-set 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.