Boolean Feature Discovery in Empirical Learning
Machine Learning
Data preparation for data mining
Data preparation for data mining
Communications of the ACM
Data mining: building competitive advantage
Data mining: building competitive advantage
Principles of data mining
Data Mining and Uncertain Reasoning: An Integrated Approach
Data Mining and Uncertain Reasoning: An Integrated Approach
Mastering Data Mining: The Art and Science of Customer Relationship Management
Mastering Data Mining: The Art and Science of Customer Relationship Management
Knowledge Discovery for Business Information Systems
Knowledge Discovery for Business Information Systems
Data snooping, dredging and fishing: the dark side of data mining a SIGKDD99 panel report
ACM SIGKDD Explorations Newsletter
Journal of Management Information Systems - Special section: Data mining
Hi-index | 0.00 |
Although Data Mining (DM) may often seem a highly effective tool for companies to be using in their business endeavors, there are a number of pitfalls and/or barriers that may impede these firms from properly budgeting for DM projects in the short term. This chapter indicates that the pitfalls of DM can be categorized into several distinct categories. We explore the issues of accessibility and usability, affordability and efficiency, scalability and adaptability, systematic patterns vs. sample-specific patterns, explanatory factors vs. random variables, segmentation vs. sampling, accuracy and cohesiveness, and standardization and verification. Finally, we present the technical challenges regarding the pitfalls of DM.