Data Mining Tools See5 and C5.0: Data mining is all about extracting patterns from an organization’s stored or warehoused data. These patterns can be used to gain insight into aspects of the organization’s operations, and to predict outcomes for future situations as an aid to decision-making. Patterns often concern the categories to which situations belong. For example, is a loan applicant creditworthy or not? Will a certain segment of the population ignore a mailout or respond to it? Will a process give high, medium, or low yield on a batch of raw material? See5 (Windows Xp/Vista/7/8) and its Unix counterpart C5.0 are sophisticated data mining tools for discovering patterns that delineate categories, assembling them into classifiers, and using them to make predictions. Some important features: See5/C5.0 has been designed to analyze substantial databases containing thousands to millions of records and tens to hundreds of numeric, time, date, or nominal fields. See5/C5.0 also takes advantage of computers with up to eight cores in one or more CPUs (including Intel Hyper-Threading) to speed up the analysis. To maximize interpretability, See5/C5.0 classifiers are expressed as decision trees or sets of if-then rules, forms that are generally easier to understand than neural networks. See5/C5.0 is available for Windows Xp/Vista/7/8 and Linux. See5/C5.0 is easy to use and does not presume any special knowledge of Statistics or Machine Learning (although these don’t hurt, either!) RuleQuest provides C source code so that classifiers constructed by See5/C5.0 can be embedded in your organization’s own systems.

References in zbMATH (referenced in 8 articles )

Showing results 1 to 8 of 8.
Sorted by year (citations)

  1. Yukinobu Hamuro; Masakazu Nakamoto; Stephane Cheung; Edward Ip: mbonsai: Application Package for Sequence Classification by Tree Methodology (2018) not zbMATH
  2. Reif, Matthias; Shafait, Faisal; Goldstein, Markus; Breuel, Thomas; Dengel, Andreas: Automatic classifier selection for non-experts (2014) ioport
  3. Al-Diabat, Mofleh: Arabic text categorization using classification rule mining (2012) ioport
  4. Tekli, Joe; Chbeir, Richard; Yetongnon, Kokou: An overview on XML similarity: background, current trends and future directions (2009)
  5. Kooptiwoot, S.; Salam, M. A.: IUI mining: human expert guidance of information theoretic network approach (2006) ioport
  6. Öztürk, Atakan; Kayalıgil, Sinan; Özdemirel, Nur E.: Manufacturing lead time estimation using data mining (2006)
  7. Pradhan, Sameer; Hacioglu, Kadri; Krugler, Valerie; Ward, Wayne; Martin, James H.; Jurafsky, Daniel: Probabilities for support vector machines (2005) ioport
  8. Francone, F. D.; Deschaine, L. M.: Extending the boundaries of design optimization by integrating fast optimization techniques with machine-code-based, linear genetic programming (2004) ioport