Sentence examples for mining algorithms are from inspiring English sources

Data mining algorithms are being applied to detect what is loosely termed "suspicious activity" in order to ensure our "security".

Consequently, efficient data mining algorithms are in demand for analyzing these data.

Physical-driven or simulation-driven experiments and data mining algorithms are combined for the identification of input output relationships in complex manufacturing processes.

In this paper, two data mining algorithms are applied to build a churn prediction model using credit card data collected from a real Chinese bank.

While data mining algorithms are often designed to operate on centralized data, in practice data is often acquired and stored in a distributed manner.

However, most existing association rule mining algorithms are rendered impractical on gene expression data, which typically contains thousands or tens of thousands of columns, but only tens of rows.

Data mining algorithms were applied to analyze the VOC and label data within a 10 minute window around a given measurement datapoint (5 minutes backwards and 5 minutes forwards).

Data mining algorithms were applied separately for each country and evaluation run to search for associations across several dimensions, including bull origin, type of proof, age of bull, and number of daughters.

During the IEEE 2006 International Conference on Data Mining (ICDM), the top-ten data mining algorithms were defined based on expert nominations, citation counts, and a community survey.

In output privacy, generally interest is in problems such as association rule hiding and query auditing where the output of different data mining algorithms is perturbed or audited in order to preserve privacy.

The relative efficiency of data mining algorithms is an important consideration when comparing various approaches.