ComputingComputer ScienceArtificial IntelligenceData MiningMachine LearningNeural NetworksPattern RecognitionHuman Intelligence
Companies have amassed large data warehouses over decades, yet most fail to unlock their value, despite the availability of techniques such as neural networks, regression, and decision trees, with many practitioners wary of neural networks’ black‑box nature. This paper reviews artificial neural networks and examines whether they should remain the preferred tool for data mining. The authors provide an overview of artificial neural networks and assess their suitability relative to other data mining methods. The authors conclude that data mining is the answer to extracting value from large data warehouses.
Companies have been collecting data for decades, building massive data warehouses in which to store it. Even though this data is available, very few companies have been able to realize the actual value stored in it. The question these companies are asking is how to extract this value. The answer is Data mining. There are many technologies available to data mining practitioners, including Artificial Neural Networks, Regression, and Decision Trees. Many practitioners are wary of Neural Networks due to their black box nature, even though they have proven themselves in many situations. This paper is an overview of artificial neural networks and questions their position as a preferred tool by data mining practitioners.
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An introduction to neural networks
Choice Reviews Online · 1998 · 1.8K citations