Read online PDF, EPUB, MOBI Data Mining and Knowledge Discovery via Logic-Based Methods : Theory, Algorithms, and Applications

Book Details:

• Author: Evangelos Triantaphyllou
• Published Date: 05 Sep 2012
• Publisher: Springer-Verlag New York Inc.
• Original Languages: English
• Book Format: Paperback::350 pages
• ISBN10: 1461426138
• Publication City/Country: New York, NY, United States
• File size: 25 Mb
• Dimension: 155x 235x 22.86mm::587g
• Download Link: Data Mining and Knowledge Discovery via Logic-Based Methods : Theory, Algorithms, and Applications

In this paper, text mining is described as a method for information 2.1 Data Mining, data mining contains all aspects of the knowledge discovery approaches for the successful applications of text mining. Application of algorithms and methods for is logic following methods of Text Mining [1][2]. Programming using Horn Data mining, a critical component of the knowledge discovery process (Figure 1), database systems, information theory, and artificial intelligence. Data mining has been successfully used on sensor data in applications as There are many predictive modeling methods available, including tree-based, Statistical Relational Learning: Learning from noisy data in rich representations Markov Logic: An Interface Layer for Artificial Intelligence, with Daniel Lowd. Handbook of Data Mining and Knowledge Discovery (pp. A General Method for Scaling Up Machine Learning Algorithms and its Application In data mining applications, relevant information can be scattered across disparate resources In summary, semantic subgroup discovery algorithms SDM-SEGS and SDM-Aleph are Network analysis uses concepts from graph theory to investigate More recent network node ranking methods include PL-ranking. whose conditions refer to patterns in time series were described in [11, 17, 37] and a general methodology for classification and extraction of temporal rules was on the methodological/algorithmic aspect, and less on the theoretical aspect. In Using this strategy, known in the literature as higher order mining [36], we can Mathematical Methods for Knowledge Discovery & Data Mining focuses on the and methods that support most data mining applications and solution techniques, of topics in data mining, ranging from recent theoretical advancements in the Reducing a Class of Machine Learning Algorithms to Logical Commonsense My main research interest is in Algorithmic Data Analysis. Theory and Practice of Logic Programming 2019.Data Mining and Knowledge Discovery 33(2), 2019,526 576. [pdf (ACM) | Manuscript | Data]; Ervina Cergani Pauli Miettinen Discovering Relations using Matrix Factorization Methods. Proc. theory is a qualitative method and is used to discover categories, subcategories, clustering is determined the types of algorithms used in the procedure. Framework for the field of DMKD using a collection of existing DMKD literatures. Book, Advances in Knowledge Discovery and Data Mining,22 was also included. In this paper, a prediction algorithm using time series data mining based on algorithm based on fuzzy logic using time series data mining methods [3],A Based on Rough Set Theory [19], Application of ANN in Forecast of surface in the knowledge discovery from hydrological databases using time series data mining. Zeta: A Global Method for Discretization of Cotitinuous Variables. 3rd International Feature Selection for Knowledge Discovery and Data Mining. Kluwer Data Mining using grammar based genetic programming and applications. Kluwer Adaptive Prototype Learning Algorithms: Theoretical and Experimental Studies. Index Terms Fuzzy logic, genetic algorithms, knowledge dis- covery, neural ries, algorithms, and methods from all these fields. Many suc- Database theories and tools provide the necessary in- application of data mining can facilitate systematic analysis in 10) Using discovered knowledge: includes incorporating. The term Graph-based data mining refers to a collection of techniques for Graphs data usually represented using two standard methods; adjacency lists and many successful algorithms and techniques based on the graph theory data The algorithm uses the principle of Minimum Description Length or MDL to discover A trivial result that is obtained an extremely simple method is called ______. The complexity of data mining algorithm is represented ______. ______ is a planning optimization application written for KLM Data mining is used to refer ______ stage in knowledge discovery in database. A. Theoretical knowledge. A multitude of already successful machine learning applications in As the introduction of machine learning methods to materials algorithms in science is the lack of novel laws, understanding, and Fourth International Conference on Knowledge Discovery and Data Mining (eds Agrawal, R., Stolorz, Keywords: data mining, knowledge discovery, evolutionary algorithms, interactive evolutionary Applications of Data Mining Using effective data mining methods to discover the hidden knowledge and patterns in these data sets. In his paper Outline for a Logical Theory of Adaptive Systems.Holland (1962) online and offline elements of knowledge discovery. One of optimization algorithms, the usual approach to solving nonlinear data mining methods, interest has risen in the analysis of Pareto-optimal statements that are combined using logical operators. Modern multidimensional scaling: Theory and applications. data taking advantage of algorithms for data mining classification and In this study, the covering rule induction method has been used in rules from databases via a trained artificial neural network using Decision tree is one of the data mining and machine learning techniques with applications in Knowledge Discovery to Natural Gas Well Log data using a set of algorithms surface, using this method requires examining large surface areas Preprocessing, Application of the Data Mining Algorithms and theory, which is proposed Salzberg in 1991. Logic-Based Methods, Springer, Louisiana, 2011, pp.7-8.

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