OLAP Versus Data Mining

OLAP is a data summarization/aggregation tool that helps simplify data analysis, while data
mining allows the automated discovery of implicit patterns and interesting knowledge hidden in large amounts of data.
Key is OLAP

OLAP tools are targeted toward simplifying and supporting interactive data analysis,
whereas the goal of data mining tools is to automate as much of the process as possible,
while still allowing users to guide the process. In this sense, data mining goes one step beyond traditional on-line analytical processing.
Key is OLAP tools

OLAP functions are essentially for user-directed data summary and comparison
(by drilling, pivoting, slicing,dicing, and other operations). Data mining covers a much broader spectrum than simple OLAP operations because it performs not only data summary and comparison but also association, classification, prediction, clustering, time-series analysis, and other data analysis tasks.
Key is OLAP Operations

Data mining is not confined to the analysis of data stored in data warehouses. It may
analyze data existing at more detailed granularities than the summarized data provided
in a data warehouse. It may also analyze transactional, spatial, textual, and multimedia
data that are difficult to model with current multidimensional database technology. In
this context, data mining covers a broader spectrum than OLAP with respect to data
mining functionality and the complexity of the data handled.
Key is OLAP data analysis

Because data mining involves more automated and deeper analysis than OLAP,
data mining is expected to have broader applications. Data mining can help business
managers find and reach more suitable customers, as well as gain critical
business insights that may help drive market share and raise profits. In addition,
data mining can help managers understand customer group characteristics
and develop optimal pricing strategies accordingly, correct item bundling based
not on intuition but on actual item groups derived from customer purchase patterns,
reduce promotional spending, and at the same time increase the overall net
effectiveness of promotions.