Three perspectives of data mining
type: Item
instance of: scholarly article
title: Three perspectives of data mining
zbMATH Open document ID: 1011.68031
DOI: 10.1016/S0004-3702(02)00357-0
author: Zhi-Hua Zhou
published in: Artificial Intelligence
publication date: 2003-01-22T00:00:00Z
review text: This paper reviews three recent books on data mining written from three different perspectives, i.e., databases, machine learning, and statistics. Although the exploration in this paper is suggestive instead of conclusive, it reveals that besides some common properties, different perspectives lay strong emphases on different aspects of data mining. The emphasis of the database perspective is on efficiency because this perspective strongly concerns the whole discovery process and huge data volume. The emphasis of the machine learning perspective is on effectiveness because this perspective is heavily attracted by substantive heuristics working well in data analysis although they may not always be useful. As for the statistics perspective, its emphasis is on validity because this perspective cares much for mathematical soundness behind mining methods.
Mathematics Subject Classification ID: 68P15
zbMATH DE Number: 1857178
zbMATH Keywords: data mining
zbMATH Keywords: databases
zbMATH Keywords: machine learning
zbMATH Keywords: statistics
describes a project that uses: UCI-ml
MaRDI profile type: Publication
Recommended article: Q3526753
Recommended article: Q4208553
Recommended article: 3DM: Domain-oriented Data-driven Data Mining
Recommended article: Q4787572
description: scientific article; zbMATH DE number 1857178