Sentence examples for methods for data mining from inspiring English sources

The dynamic nature of information resources as well as the continuous changes in the information demands of the users has made it very difficult to provide effective methods for data mining and document ranking.

They have found that majority of the existing literature is focused on innovative methods for data mining and analysis.

Like the statistical analysis, the problem specific methods for data mining also attempted to understand the meaning from the collected data.

Scientific fields, such as digital signal processing (DSP) and computer science (particularly machine learning), provide efficient and mathematically well-defined methods for data mining and knowledge discovery from specific observations or databases [1].

Table 2 Efficient data analytics methods for data mining Problem Method References Clustering BIRCH [44] DBSCAN [45] Incremental DBSCAN [46] RKM [47] TKM [42] Classification SLIQ [50] TLAESA [51] FastNN [52] SFFS [53] GPU-based SVM [43] Association rules CLOSET [54] FP-tree [32] CHARM [55] MAFIA [56] FAST [57] Sequential patterns SPADE [58] CloSpan [59] PrefixSpan [60] SPAM [61] ISE [62].

We focus on computational methods for data mining and machine learning and bioinformatics methods for incorporating prior biological knowledge into data analysis algorithms.

In the present work we applied the method for data mining of Jamu formulas accumulated in KNApSAcK database.

The Connectivity Map is a reference collection of expression profiles generated by stimulation of human cultured cells with a number of small molecules, along with an algorithmic method for data mining through pattern matching.

Although technical variations, such as different miRNA array platforms used and bioinformatics methods applied for data mining, may partially explain the inconsistency, it is possible that miRNA prognostic signature may differ across populations.

The assumptions we make about this complexity greatly influences the analytical methods we choose for data mining and, in turn, our results and inferences.

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.