An overview of actionable knowledge discovery techniques

The amazing progresses achieved in both data collecting and transferring have confronted us with a vast volume of stored and transient data. Analyzing such data can result in valuable knowledge providing a competitive advantage in support of decision-making. However, the high volume of the data makes it impossible to analyze such data manually. Data mining methods have been developed to automate this process. These methods extract useful knowledge from a massive amount of data. The vast majority of available methods focus on finding different types of patterns from various kinds of data whereas a few of them pay enough attention to the usability of mined patterns. Subsequently, there is a noticeable gap between delivered patterns and business expectations. Actionable Knowledge Discovery (AKD) is motivated to narrow this gap. Up to now, many AKD methods have been proposed. However, there is no comprehensive survey summarizing different aspects of such methods. Moreover, the lack of clear definitions and boundaries in this area makes it challenging to detect comparable methods. This paper aims at clarifying definitions and boundaries of AKD area. In this regard, some viewpoints are defined, and AKD methods are categorized by use of them. In addition, AKD methods are reviewed, and finally, a characterization table is presented that concludes the survey and can be used for studying different methods in AKD area.

Automated summary

In summary, data mining methods are developed to extract valuable knowledge from a vast amount of data to support decision-making. However, there is a noticeable gap between delivered patterns and business expectations, and Actionable Knowledge Discovery (AKD) aims to narrow this gap.