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Before data mining algorithms can be used, a target data set must be assembled. As data mining can only uncover patterns actually present in the data, the target data set must be large enough to contain these patterns while remaining concise enough to be mined within an acceptable time limit. A common source for data is a data mart or data ...
Download as PDF; Printable version; In other projects Wikidata item; ... Pages in category "Data mining algorithms" The following 6 pages are in this category, out of ...
Download as PDF; Printable version; ... Data mining facilities are included in some of the Category: ... Data mining algorithms (2 C, 6 P)
When developing new algorithms or index structures, the existing components can be easily reused, and the type safety of Java detects many programming errors at compile time. ELKI is a free tool for analyzing data, mainly focusing on finding patterns and unusual data points without needing labels.
Audio under Creative Commons from 100k songs (343 days, 1TiB) with a hierarchy of 161 genres, metadata, user data, free-form text. Raw audio and audio features. 106,574 Text, MP3 Classification, recommendation 2017 [143] M. Defferrard et al. Bach Choral Harmony Dataset Bach chorale chords. Audio features extracted. 5665 Text Classification 2014
Evolutionary data mining, or genetic data mining is an umbrella term for any data mining using evolutionary algorithms.While it can be used for mining data from DNA sequences, [1] it is not limited to biological contexts and can be used in any classification-based prediction scenario, which helps "predict the value ... of a user-specified goal attribute based on the values of other attributes."
Tanagra is a free suite of machine learning software for research and academic purposes developed by Ricco Rakotomalala at the Lumière University Lyon 2, France. [1] [2] Tanagra supports several standard data mining tasks such as: Visualization, Descriptive statistics, Instance selection, feature selection, feature construction, regression, factor analysis, clustering, classification and ...
This kind of data redundancy due to the spatial correlation between sensor observations inspires the techniques for in-network data aggregation and mining. By measuring the spatial correlation between data sampled by different sensors, a wide class of specialized algorithms can be developed to develop more efficient spatial data mining algorithms.