Benchmarking Data Mining Algorithms

Abstract

Data mining is the process of sifting through the mass of organizational (internal and external) data to identify patterns critical for decision support. Successful implementation of the data mining effort requires a careful assessment of the various tools and algorithms available. The basic premise of this study is that machine-learning algorithms, which are assumption free, should outperform their traditional counterparts when mining business databases. The objective of this study is to test this proposition by investigating the performance of the algorithms for several scenarios. The scenarios are based on simulations designed to reflect the extent to which typical statistical assumptions are violated in the business domain. The results of the computational experiments support the proposition that machine learning algorithms generally outperform their statistical counterparts under certain conditions. These can be used as prescriptive guidelines for the applicability of data mining techniques.

Decision-making Support Systems Using Case-based Reasoning for Construction Project Delivery Method Selection: Focused on the Road Construction Projects in Korea

“…Most of the previous studies on the selection of the project delivery method in the construction sector analyzed the accumulated data using traditional statistical techniques. Such methodology, however, is limited in that the results will vary slightly depending on how the statistical hypotheses are defined, such as the generalizations of the required data [12]. Conversely, relatively objective values can be derived by analyzing the data using the artificial intelligence (AI) technique through data mining, because this method derives analysis results based on the accumulated data through machine learning, without resorting to statistical hypotheses.…”

Data Mining; A Conceptual Overview

“…Complexity arises from anomalies such as discontinuity, noise, ambiguity, and incompleteness . And while most data mining algorithms are able to separate the effects of such irrelevant attributes in determining the actual pattern, the predictive power of the mining algorithms may decrease as the number of these anomalies increase [Rajagopalan and Krovi, 2002].…”

Cross-Correlation Measure for Mining Spatio-Temporal Patterns

“…As a subfield of data mining (Fayyad & Uthurusamy, 1996;Rajagopalan & Krovi, 2002), spatio-temporal data mining studies the discovery of interesting, implicit relationships and characteristics from spatio-temporal data (Koperski, Han, & Adhikary, 1998;Yao, 2003). This field has been attracting significant research interest in recent years, driven by the increasing availability of large datasets containing important spatial and temporal elements across a wide spectrum of application domains.…”