Sentence examples for standard data mining from inspiring English sources

This chapter introduces the CRISP-DM standard data mining process and characterizes how JDM supports the various phases of this process.

Standard data mining techniques were used to extract and annotate free text and structured data to assess urinary incontinence recorded within the EHRs.A total 5,349 prostate cancer patients were identified in our EHR-system between 1998-2013.

Standard data mining algorithms can accurately and efficiently identify these outcomes in existing EHRs; the complete assessment of these outcomes is essential to move practice into the patient-centered realm of healthcare.

In standard data mining approach to text categorization, documents represent as bag-of-word vectors.

Although aspects of weighted network analysis relate to standard data mining methods, the intuitive network language and analysis framework transcend any particular analysis method.

Most classifiers assume equal weighting of the classes in terms of both the number of instances and the level of importance - misclassifying class A has the same importance as misclassifying class B. However, when trying to predict a minority class in an imbalanced dataset or when a false negative is deemed more important than a false positive, standard data mining techniques are not successful.

For this, the framework fuses two highly relevant approaches and embeds these into the building context: the generic model-based design methodology for cyber-physical systems and the cross-industry standard process for data mining.

Furthermore, the manufacturing standard, quality control and data mining for the impact bar design is urgent to exploited in the future.

Despite being widely used across many industries to solve business problems, previously there was no data mining standard for Java that allowed applications to include data mining solutions that were portable across multiple (DMEs).

Hadoop, based on Google MapReduce [5] and Google distributed File System (GFS) [6], has become the de facto standard tool for distributed data mining in the academic and industrial world.

It is in fact necessary to remove them; data-mining techniques such as feature selection assume that there is no redundancy in a dataset (deletion of redundant data items is a standard preprocessing step in data mining), and so the presence of redundant sequences would undermine our results.