Search Results - (( based optimization based algorithm ) OR ( data extraction mining algorithm ))
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Social media mining: a genetic based multiobjective clustering approach to topic modelling
Published 2021“…This paper investigates the effects of using a multiobjective genetic algorithm (MOGA) based clustering technique to cluster texts for topic extraction which is designed based on the structure and purity of the clusters in order to determine the optimal initial centroids and the number of clusters, k. …”
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Social media mining: a genetic based multiobjective clustering approach to topic modelling
Published 2021“…This paper investigates the effects of using a multiobjective genetic algorithm (MOGA) based clustering technique to cluster texts for topic extraction which is designed based on the structure and purity of the clusters in order to determine the optimal initial centroids and the number of clusters, k. …”
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An online density-based clustering algorithm for data stream based on local optimal radius and cluster pruning
Published 2019“…Data stream clustering plays an important role in data stream mining for knowledge extraction. …”
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4
A buffer-based online clustering for evolving data stream
Published 2019“…Data stream clustering plays an important role in data stream mining for knowledge extraction. …”
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An efficient IDS using hybrid Magnetic swarm optimization in WANETs
Published 2018“…In this paper, we propose an efficient IDS based on hybrid heuristic optimization algorithm which is inspired by magnetic field theory in physics that deals with attraction between particles scattered in the search space. …”
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An Efficient IDS Using Hybrid Magnetic Swarm Optimization in WANETs
Published 2018“…In this paper, we propose an efficient IDS based on hybrid heuristic optimization algorithm which is inspired by magnetic field theory in physics that deals with attraction between particles scattered in the search space. …”
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Logic Mining Approach: Shoppers’ Purchasing Data Extraction via Evolutionary Algorithm
Published 2023“…In reducing the learning complexity, a genetic algorithm was implemented to optimize the logical rule throughout the learning phase in performing a 2-satisfiability-based reverse analysis method, implemented during the learning phase as this method was compared. …”
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Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli
Published 2018“…To assess the applicability and accuracy of the proposed method for long-term electrical energy consumption, its estimates are compared with those obtained from artificial neural network (ANN), support vector regression (SVR), adaptive neuro-fuzzy inference system (ANFIS), rule-based data mining algorithm, GEP, linear, quadratic and exponential models optimized by particle swarm optimization (PSO), cuckoo search algorithm (CSA), artificial cooperative search (ACS) algorithm and backtracking search algorithm (BSA). …”
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A new model for iris data set classification based on linear support vector machine parameter's optimization
Published 2020“…Data mining is known as the process of detection concerning patterns from essential amounts of data. …”
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Data normalization techniques in swarm-based forecasting models for energy commodity spot price
Published 2014“…Data mining is a fundamental technique in identifying patterns from large data sets.The extracted facts and patterns contribute in various domains such as marketing, forecasting, and medical.Prior to that, data are consolidated so that the resulting mining process may be more efficient.This study investigates the effect of different data normalization techniques.which are Min-max, Z-score and decimal scaling, on Swarm-based forecasting models.Recent swarm intelligence algorithms employed includes the Grey Wolf Optimizer (GWO) and Artificial Bee Colony (ABC).Forecasting models are later developed to predict the daily spot price of crude oil and gasoline.Results showed that GWO works better with Z-score normalization technique while ABC produces better accuracy with the Min-Max.Nevertheless, the GWO is more superior than ABC as its model generates the highest accuracy for both crude oil and gasoline price.Such a result indicates that GWO is a promising competitor in the family of swarm intelligence algorithms.…”
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Clustering of rainfall data using k-means algorithm
Published 2019“…Clustering algorithms in data mining is the method for extracting useful information for a given data. …”
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Improved building roof type classification using correlation-based feature selection and gain ratio algorithms
Published 2017“…First, the feature importance was evaluated using gain ratio algorithm, and the result was ranked, leading to selection of the optimal feature subset. …”
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Improvement on rooftop classification of worldview-3 imagery using object-based image analysis
Published 2019“…Then, the classifier (support vector machine (SVM) and data mining (DM) algorithm, decision tree (DT) were applied on each fusion image and their accuracy were evaluated. …”
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14
Classification with degree of importance of attributes for stock market data mining
Published 2004“…The SVM is a training algorithm for learning classification and regression rules from data [7]. …”
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Class binarization with self-adaptive algorithm to improve human activity recognition
Published 2018“…Two publicly activity datasets are used; Wireless Sensor Data Mining (WISDM) and Physical Activity Monitoring for Aging People (PAMAP2). …”
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Clustering algorithm for market-basket analysis : the underlying concept of data mining technology
Published 2003“…The goal of data mining is to extract interesting correlated information from large databases. …”
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Data mining using genetic algorithm in finance data / A. Noor Latiffah and A. B. Nordin
Published 2006“…Data mining can discover patterns or rules from a vast volume of data. …”
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A novel ensemble decision tree-based CHi-squared Automatic Interaction Detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping
Published 2014“…An ensemble algorithm of data mining decision tree (DT)-based CHi-squared Automatic Interaction Detection (CHAID) is widely used for prediction analysis in variety of applications. …”
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Direct approach for mining association rules from structured XML data
Published 2012“…Having the ability to extract information from XML data would answer the problem of mining the web contents which is a very useful and required power nowadays. …”
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