As explained, the use of the association rules mining algorithms performed well.
The challenges of using these methods occur both when using ontologies and ontology-annotated data as the target of a machine learning and data mining algorithm as well as when using ontologies and ontology-annotated data as features.
The conclusions are relevant for everyone who is developing mutation mining tools, probably most users of text mining algorithms, and they are well justified.
ChloroMitoSSRDB 2.00 also provides users with an option to export the search results obtained from two different repeat mining algorithms, IMEx (21) and MISA, as well as the repeat information in EXCEL compatible format, to utilize the information for further downstream processing of the observed repeats in user-specified organelle genome.
Among them, it is possible to mention the execution of more complex query types (e.g., similarity queries) and several well-known data mining algorithms (e.g., data clustering) that are directly based on similarity computations.
Decide matches hundreds of thousands of devices to their "model lineages" and crawls the Web for news and rumors, as well as applying "advanced machine learning and text mining algorithms" to predict future product releases.
Apriori [1] and FP-Growth [6] are well-known BFS and DFS frequent pattern mining algorithms respectively.
While statistical analysis of graphs or sets, or measures of semantic similarity, are well established methods that use ontologies for data mining, many machine learning and data mining algorithms that are applied to unstructured data are not yet widely used with ontologies and ontology-structured data.
For example, most available data mining algorithms are not very suitable for big data mining applications as their design is based on limited and well defined data sets [33].
Data mining algorithms are being applied to detect what is loosely termed "suspicious activity" in order to ensure our "security".
