Search results
Results from the WOW.Com Content Network
The following aspects of each given implementation are measured: [2] overall user runtime; peak memory allocation; gzip'ped size of the solution's source code; sum of total CPU time over all threads; individual CPU utilization; It is common to see multiple solutions in the same programming language for the same problem.
Different text mining methods are used based on their suitability for a data set. Text mining is the process of extracting data from unstructured text and finding patterns or relations. Below is a list of text mining methodologies. Centroid-based Clustering: Unsupervised learning method. Clusters are determined based on data points. [1]
With evaluation is meant the level of achieving the target for a particular evaluation item. There are general "methods" respectively approaches as well as IT-supported "software tools" that enable an effective and efficient work. The following is a list of notable methods and benchmarking software tools.
Orange with its text mining add-on. The PLOS Text Mining Collection. [3] The programming language R provides a framework for text mining applications in the package tm. [4] The Natural Language Processing task view contains tm and other text mining library packages. [5] spaCy – open-source Natural Language Processing library for Python
Text mining, text data mining (TDM) or text analytics is the process of deriving high-quality information from text. It involves "the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources." [1] Written resources may include websites, books, emails, reviews, and
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 ...
The artificial landscapes presented herein for single-objective optimization problems are taken from Bäck, [1] Haupt et al. [2] and from Rody Oldenhuis software. [3] Given the number of problems (55 in total), just a few are presented here. The test functions used to evaluate the algorithms for MOP were taken from Deb, [4] Binh et al. [5] and ...
For each computer system, the following quantities are reported: [2] R max – the performance in GFLOPS for the largest problem run on a machine. N max – the size of the largest problem run on a machine. N 1/2 – the size where half the R max execution rate is achieved. R peak – the theoretical peak performance GFLOPS for the machine.