Data mining: past, present and future

2011/02/07 by Frans Coenen · 64 citations
Computer Science · #Data Mining Algorithms and Applications #Face and Expression Recognition #Machine Learning and Data Classification #Computer science #Data science #Domain (mathematical analysis) #Knowledge extraction #Scale (ratio) #Data mining #Artificial intelligence

paper · doi:10.1017/s0269888910000378

published in The Knowledge Engineering Review 26(1), 25-29 (Cambridge University Press)

openalex publication_date 2011/02/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/05/21

Abstract

Abstract Data mining has become a well-established discipline within the domain of artificial intelligence (AI) and knowledge engineering (KE). It has its roots in machine learning and statistics, but encompasses other areas of computer science. It has received much interest over the last decade as advances in computer hardware have provided the processing power to enable large-scale data mining to be conducted. Unlike other innovations in AI and KE, data mining can be argued to be an application rather then a technology and thus can be expected to remain topical for the foreseeable future. This paper presents a brief review of the history of data mining, up to the present day, and some insights into future directions.

Citations

• Idiot's Bayes—Not So Stupid After All?
• The KDD process for extracting useful knowledge from volumes of data
• Mining frequent patterns without candidate generation
• Mining association rules between sets of items in large databases

Cited by

• Review of business intelligence through data analysis
• Data mining [wikipedia]

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