Using <scp>AHP</scp> and <scp>TOPSIS</scp> for Goal‐Oriented Data Mining With Effective Usage

ABSTRACT Digital technology is crucial in modern society for devices generating vast amounts of data. Effective data processing is essential for everyday operations, and data mining approaches are crucial in industries like health, education, and business. By reporting data breaches, these strategies protect private data and help identify fraud in digital transactions. A vast amount of data is generated as a result of the development of information and communication technologies, which have made many resources for data production and collection available. An effective and efficient method is needed to process this data and extract valuable information. The current study emphasizes how vital data mining techniques are for finding and collecting valuable information from the data that is accessible. We have carefully examined the current methods to extract several features‐based characteristics from the literature. The study introduces a systematic method for selecting an effective data mining methodology and prioritizes various data mining paradigms based on the most frequently utilized features. Based on the analytic hierarchy process (AHP) and the technique for preference by similarity to the ideal solution (TOPSIS), the salient aspects are chosen from these features to rate the data mining approaches. The consultative AHP and TOPSIS evaluation support system aids designers in determining key goal‐oriented data mining design objectives and optimal conceptual alternatives for detailed development, enhancing their ability to compute weighting values in competitive benchmarking stages. The study assists users in selecting an efficient data mining process to extract information from unprocessed data.