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Semantic data mining is a subset of data mining that specifically seeks to incorporate domain knowledge, such as formal semantics, into the data mining process.Domain knowledge is the knowledge of the environment the data was processed in. Domain knowledge can have a positive influence on many aspects of data mining, such as filtering out redundant or inconsistent data during the preprocessing ...
Given the variety of data sources (e.g. databases, business applications) that provide data and formats that data can arrive in, data preparation can be quite involved and complex. There are many tools and technologies [5] that are used for data preparation. The cost of cleaning the data should always be balanced against the value of the ...
Orange is an open-source software package released under GPL and hosted on GitHub.Versions up to 3.0 include core components in C++ with wrappers in Python.From version 3.0 onwards, Orange uses common Python open-source libraries for scientific computing, such as numpy, scipy and scikit-learn, while its graphical user interface operates within the cross-platform Qt framework.
Instance selection (or dataset reduction, or dataset condensation) is an important data pre-processing step that can be applied in many machine learning (or data mining) tasks. [1] Approaches for instance selection can be applied for reducing the original dataset to a manageable volume, leading to a reduction of the computational resources that ...
KNIME workflows can be used as data sets to create report templates that can be exported to document formats such as doc, ppt, xls, pdf and others. Other capabilities of KNIME are: KNIMEs core-architecture allows processing of large data volumes that are only limited by the available hard disk space (not limited to the available RAM). E.g.
fastqp Simple FASTQ quality assessment using Python. Kraken: [9] A set of tools for quality control and analysis of high-throughput sequence data. HTSeq [10] The Python script htseq-qa takes a file with sequencing reads (either raw or aligned reads) and produces a PDF file with useful plots to assess the technical quality of a run.
Most preprocessors are specific to a particular data processing task (e.g., compiling the C language). A preprocessor may be promoted as being general purpose , meaning that it is not aimed at a specific usage or programming language, and is intended to be used for a wide variety of text processing tasks.
It was observed that data scientists would write machine learning algorithms in languages such as R and Python for small data. When it came time to scale to big data, a systems programmer would be needed to scale the algorithm in a language such as Scala. This process typically involved days or weeks per iteration, and errors would occur ...