Sentence examples for mining and clustering from inspiring English sources

While there are many text mining and clustering methods, we chose topic modelling (TM, [17, 18]) because this method captures two aspects that are important for this dataset: words may have multiple meanings or interpretations and documents may contain one or more topics.

The property of cosines can be used to evaluate the similarity between trends of data progression and has been widely adapted in data mining and clustering strategies [ 24- 26].

Text mining and clustering can be applied here to get the interest fields implicitly revealed by the user.

Moreover, future research will investigate the performance of the EADE algorithm in solving constrained and multi-objective optimization problems as well as real-world applications such as data mining and clustering problems.

In this paper, we adapt and extend association rule mining and clustering algorithms to extract useful knowledge regarding diabetes and high blood pressure from the 1999 2008 survey results, thus demonstrating how data mining techniques may be used to support evidence-based medicine.

The data mining and clustering algorithm has been programmed using the Weka tool (University of Waikato, New Zealand), by implementing the Expectation Maximization (EM) clustering technique.

Data mining is mainly composed of association rules mining, sequent pattern mining, classification and clustering.

CIG-DB performs literature mining, training, and clustering to semi-automatically classify T cell receptors (TCR) and immunoglobulins (IG) for human and mouse into two groups: cancer therapy and hematological tumors.

This method uses sophisticated text data mining (categorizing and clustering using words and multiword phrases) in conjunction with NIH-wide definitions used to match projects to categories, while improving consistency and eliminating variability in the definition of the research categories reported.

The decomposition is driven by data mining, software metrics, and clustering techniques.

The experimental study and evaluation show that the proposed approach outperforms the peer approaches, i.e. opinion mining and clustering-summarization, in terms of users' responsiveness and its ability to discover the most important topics.