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Atomic data mining Numerical Methods, Source Code SQlite with Python

Procedia - Social and Behavioral Sciences 2013年 73卷 232-239页

作者： Ali Khwaldeh Amani Tahat Jordi Marti Mofleh Tahat Department Of Computer Engineering Faculty of engineering Philadelphia University 19392 Amman Jordan Department of Physics and Nuclear Engineering Technical University of Catalonia - Barcelona Tech B5-209 North Campus UPC 08034 Barcelona Catalonia Spain Software Development Engineer American Airlines® Addison TX | HDQ2 3N2D-63

This paper introduces a recently published Python data mining book (chapters, topics, samples of Python source code written by its authors) to be used in data mining via world wide web and any specific database in several disciplines (economic, physics, education, marketing. etc). The book started with an introduction to data mining by explaining some of the data mining tasks involved classification, dependence modelling, clustering and discovery of association rules. The book addressed that using Python in data mining has been gaining some interest from data miner community due to its open source, general purpose programming and web scripting language; furthermore, it is a cross platform and it can be run on a wide variety of operating systens such as Linux, Windows, FreeBSD, Macintosh, Solaris, OS/2, Amiga, AROS, AS/400, BeOS, OS/390, z/OS, Palm OS, QNX, VMS, Psion, Acorn RISC OS, VxWorks, PlayStation, Sharp Zaurus, Windows CE and even PocketPC. Finally this book can be considered as a teaching textbook for data mining in which several methods such as machine learning and statistics are used to extract high-level knowledge from real-world datasets.

关键词： Python atomic data database data mining algorithms data model collaborative intelligence machine learning

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Density-Based Clustering of data Streams at Multiple Resolutions

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ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM data 2009年第3期3卷 1–28页

作者： Wan, Li Ng, Wee Keong Dang, Xuan Hong Yu, Philip S. Zhang, Kuan Nanyang Technol Univ Singapore 639798 Singapore Inst Infocomm Res Singapore Singapore Univ Illinois Chicago IL USA Singapore Management Univ Singapore Singapore

In data stream clustering, it is desirable to have algorithms that are able to detect clusters of arbitrary shape, clusters that evolve over time, and clusters with noise. Existing stream data clustering algorithms are generally based on an online-offline approach: The online component captures synopsis information from the data stream (thus, overcoming real-time and memory constraints) and the offline component generates clusters using the stored synopsis. The online-offline approach affects the overall performance of stream data clustering in various ways: the ease of deriving synopsis from streaming data;the complexity of data structure for storing and managing synopsis;and the frequency at which the offline component is used to generate clusters. In this article, we propose an algorithm that (1) computes and updates synopsis information in constant time;(2) allows users to discover clusters at multiple resolutions;(3) determines the right time for users to generate clusters from the synopsis information;(4) generates clusters of higher purity than existing algorithms;and (5) determines the right threshold function for density-based clustering based on the fading model of stream data. To the best of our knowledge, no existing data stream algorithms has all of these features. Experimental results show that our algorithm is able to detect arbitrarily shaped, evolving clusters with high quality.

关键词： data mining algorithms density based clustering evolving data streams

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Study of Social Networks as Research Entities Under the Threat of Identity and Information Vulnerability

Study of Social Networks as Research Entities Under the Thre...

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9th International Conference on Applied Human Factors and Ergonomics (AHFE) / International Conference on Human Factors in Cybersecurity

作者： Flores-Urgiles, Mariuxi Serpa-Andrade, Luis Flores-Urgiles, Cristhian Caiza-Caizabuano, Jose Ortiz-Amoroso, Martin Narvaez-Calle, Henry Santander-Paz, Marlon Guaman-Guaman, Saul Univ Catolica Cuenca Extension Canar Cuenca Ecuador Univ Politecn Salesiana Grp Invest Inteligencia Artificial & Tecnol Asist Cuenca Ecuador Univ Fuerzas Armadas ESPE Sangolqui Ecuador

ISBN: (纸本)9783319947822;9783319947815

This paper describes an awareness campaign on the correct use of social networks. Based on the results of some surveys, it describes the reality of the situation and proposes methods and strategies to modify the use of social networks in adolescents. Extraction, transformation and cleaning of the data obtained from the surveys was carried out to generate a data repository. Once the data bank was obtained, an analysis was carried out, by applying data mining algorithms, such as those of association, classification and clustering, in order to obtain patterns of behavior. We compare these results to data received from subjects prior to the training in order to determine the efficacy of the training.

关键词： Social networks Digital communication Vulnerability data mining algorithms

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Prediction of fresh herbage yield using data mining techniques with limited plant quality parameters

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SCIENTIFIC REPORTS 2024年第1期14卷 1-15页

作者： Celik, Senol Tutar, Halit Gonulal, Erdal Er, Hasan Bingol Univ Fac Agr Dept Anim Sci Biometry & Genet Unit TR-12000 Bingol Turkiye Bingol Univ Fac Agr Dept Field Crops TR-12000 Bingol Turkiye Bahri Dagdas Int Agr Res Inst TR-42000 Konya Turkiye Bingol Univ Fac Agr Dept Biosyst Engn TR-12000 Bingol Turkiye

The purpose of this study was to ascertain the fresh herbage yield, fertilizer dosage, and plant characteristics of the Sorghum-Sudangrass hybrid grown in arid and semi-arid regions, as well as their interrelationships. For this reason, data from the Sorghum-Sudangrass hybrid were used to assess the predictive performance of several data mining techniques, including CHAID, CART, MARS, and Bagging MARS. Plant traits were measured in Konya and Sanliurfa during 2021 and 2022. The descriptive statistical values were calculated as follows: plant height 306.27 cm, stem diameter 9.47 mm, fresh herbage yield 10852.51 kg/da, crude protein ratio 9.66%, acid detergent fiber 33.39%, neutral detergent fiber 51.85%, acid detergent lignin 9.76%, dry matter digestibility 62.88%, dry matter intake 2.34%, and relative feed value 114.68 (average values). The predictive capacities of the fitted models were assessed using model fit statistics such as the coefficient of determination (R-2), adjusted R-2, root mean square error (RMSE), mean absolute percentage error (MAPE), standard deviation ratio (SD ratio), and Akaike Information Criterion (AIC). With the lowest values for RMSE, MAPE, SD ratio, and AIC (246, 1.926, 0.085, and 845, respectively), and the highest R-2 value (0.993) and adjusted R-2 value (0.989), the MARS algorithm was determined to be the best model for characterizing fresh herbage yield. As a solid alternative to other data mining techniques, the MARS algorithm was shown to be the most appropriate model for forecasting fresh herbage production.

关键词： data mining algorithms Fertilizer dose Fresh herbage yield Sorghum-sudangrass hybrid

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Bio-Inspired data mining for Optimizing GPCR Function Identification

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INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2021年第4期15卷 1页

作者： Bekhouche, Safia Ben Ali, Yamina Mohamed Badji Mokhtar Univ Lab Rech Informat LRI Annaba Algeria

GPCRs are the largest family of cell surface receptors;many of them remain orphans. The GPCR functions prediction represents a very important bioinformatics task. It consists in assigning to the protein the corresponding functional class. This classification step requires a good protein representation method and a robust classification algorithm. However, the complexity of this task could be increased because of the great number of GPCRs features in most databases, which produce combinatorial explosion. In order to reduce complexity and optimize classification, the authors propose to use bio-inspired metaheuristics for both the feature selection and the choice of the best couple (feature extraction strategy [FES], data mining algorithm [DMA]). The authors propose to use the BAT algorithm for extracting the pertinent features and the genetic algorithm to choose the best couple. They compared the results they obtained with two existing algorithms. Experimental results indicate the efficiency of the proposed system.

关键词： BAT Algorithm Classification data mining algorithms Feature Extraction Strategies Feature Selection Function Prediction Genetic Algorithm GPCR

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Analysis of mutations in the COLIA1 gene with second-order rule induction

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2003年第5期17卷 721-740页

作者： Hewett, R Leuchner, J Mooney, SD Klein, TE Washington State Univ Sch Elect Engn & Comp Sci Vancouver BC Canada Univ W Florida Inst Human & Machine Cognit Pensacola FL 32501 USA Stanford Univ Sch Med Stanford CA 94305 USA

Mutations are structural changes in DNA that can cause protein malfunction and genetic disease. This paper describes a machine learning approach to analyzing mutations associated to Osteogenesis Imperfecta (OI), also known as brittle bone disease. We apply SORCER, a second-order rule induction system to predict clinical phenotypes of OI from mutation and neighboring amino acid sequences in the COLIA1 gene. On the average, over ten 10-fold cross-validations, SORCER gives more accurate results than C4.5 with average accuracy of about 81.2%. The paper discusses the advantages and limitations of SORCER and demonstrates its use to provide initial exploration of biological sequences.

关键词： machine learning induction data mining algorithms bioinformatics mutations

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Using data mining techniques to generate test cases from graph transformation systems specifications

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AUTOMATED SOFTWARE ENGINEERING 2024年第1期31卷 17-17页

作者： Araghi, Maryam Asgari Rafe, Vahid Khendek, Ferhat Concordia Univ Dept Elect & Comp Engn Montreal PQ Canada Goldsmiths Univ London Dept Comp London England Arak Univ Fac Engn Dept Comp Engn Arak *** Iran

Software testing plays a crucial role in enhancing software quality. A significant portion of the time and cost in software development is dedicated to testing. Automation, particularly in generating test cases, can greatly reduce the cost. Model-based testing aims at generating automatically test cases from models. Several model based approaches use model checking tools to automate test case generation. However, this technique faces challenges such as state space explosion and duplication of test cases. This paper introduces a novel solution based on data mining algorithms for systems specified using graph transformation systems. To overcome the aforementioned challenges, the proposed method wisely explores only a portion of the state space based on test objectives. The proposed method is implemented using the GROOVE tool set for model-checking graph transformation systems specifications. Empirical results on widely used case studies in service-oriented architecture as well as a comparison with related state-of-the-art techniques demonstrate the efficiency and superiority of the proposed approach in terms of coverage and test suite size.

关键词： Software testing Model-based testing Test case generation Model checking data mining algorithms Graph transformation systems

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A Unified Theoretical Framework for data mining

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Procedia Computer Science 2013年 17卷 104-113页

作者： Dost Muhammad Khan Nawaz Mohamudally D.K.R. Babajee Department of Computer Science & IT The Islamia University of Bahawalpur PAKISTAN School of Innovative Technologies & Engineering (SITE) University of Technology MAURITIUS Scientific & Academic Research Council African Network for Policy Research & Advocacy for Sustainability MAURITIUS

The pattern extraction and discovery of useful information from a dataset are the foremost purposes of data mining; the outcome of this process is the ‘knowledge’ which is helpful in taking the decision. For the past decade there have been multiple attempts and strong beliefs in the development and the formulation of the unified data mining frameworks that would answer to the fundamental versions related to the discovery of knowledge. In this paper we are presenting a novel unified framework for data mining conceptualized through the composite functions. The framework is further illustrated with a variety of real life datasets using different data mining algorithms.

关键词： Composite Function Unified Theory (UT) Unified data mining Theory (UDMT) Multiagent System (MAS) data mining algorithms datasets Unified data mining Framework

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Constructing Parallel Association algorithms from Function Blocks 15th

Constructing Parallel Association Algorithms from Function B...

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15th Industrial Conference on data mining (ICDM)

作者： Kholod, Ivan Kuprianov, Mikhail Shorov, Andrey St Petersburg Electrotech Univ LETI St Petersburg Russia

ISBN: (纸本)9783319209104;9783319209098

The article describes the method of construction of association rules retrieval algorithms out from function blocks having a unified interface and purely functional properties. The usage of function blocks to build association rules algorithms allows modifying the existing algorithms and building new algorithms with minimum effort. Besides, the function block properties allow to transform the algorithms into parallel form, thus improving their efficiency.

关键词： data mining Parallel data mining data mining algorithms Parallel algorithms

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Explanation Approaches for the Daphne Virtual Assistant

Explanation Approaches for the Daphne Virtual Assistant

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Viros, Antoni Selva, Daniel Texas A&M Univ Aerosp Engn College Stn TX 77843 USA

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

Explainability of Artificial Intelligence (AI) agents has become a major concern. This paper describes the different approaches to explaining decisions that are used in Daphne, a Virtual Assistant to support the early design of Earth observing systems. Feedback from past studies with expert systems engineers emphasized the importance of providing rich and transparent explanations. This paper explains how state-of-the-art techniques for AI explainability can be applied to a design tool that uses many different AI techniques. These improvements have been implemented in all Daphne roles, as well as the Question Answering (QA) system. The approaches include a revamped dialogue system, using an additive feature explanation method to explain how a neural network decides what the intent of a question is, leveraging the transparency of rule-based systems to come up with explanations in text and graphical formats, and explaining how data mining algorithms for design feature extraction and database summarizing work to end users through user-friendly text messages.

关键词： Generative Adversarial Network data mining algorithms Systems Engineering Artificial Intelligence Concurrent Engineering Machine Learning Black Box Earth Optimization Algorithm Sensitivity Analysis

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