Sentence examples for most data mining from inspiring English sources

Domingos argues that in most data mining projects, relatively little time is spent on machine learning tasks [27].

Due to the complex structures and the lack of vector representations, graph data cannot be directly used as the input for most data mining algorithms.

To the best of our knowledge, the most data mining methods have some benefits and weaknesses in malware detection subject [13].

As Fig. 4 shows, most data mining algorithms contain the initialization, data input and output, data scan, rules construction, and rules update operators [26].

Another open issue is that most data mining algorithms are designed for centralized computing; that is, they can only work on all the data at the same time.

While most data mining research focuses on algorithmic issues and aims at developing highly optimized and scalable implementations that are tailored towards specific tasks, constraint programming employs a more declarative approach.

This difficulty is not limited to the cascade model but is also commonly encountered in most data-mining methods.

Tang et al. have surveyed the ten most influential data mining algorithms in the research community [25].

In such situation most classical data mining methods became out of reach in practice to handle such big data.

Rule mining is one of the most popular data mining tools used for such purposes due to its simplicity and efficiency.

Decision tree J48 and CAR which are the most popular data mining methods are used to generate a predictive model and to find the appropriate rules.