A Review of Data Mining Techniques and Applications

Pruengkarn, Ratchakoon; Wong, Kok Wai; Fung, Chun Che

doi:10.20965/jaciii.2017.p0031

Cited by 7 publications

(3 citation statements)

References 143 publications

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“…It consists in applying statistical and artificial intelligence (AI) methods to model large data sets (Gorunescu, 2011). More precisely, DM aims at providing prediction and/or description of the data (Pruengkarn et al, 2017). On the basis of the available data, the first aim is to predict which are the more plausible values of some variables; the second aim is to search for characteristics and patterns to describe the data.…”

Section: Introductionmentioning

confidence: 99%

Agent mining approaches: an ontological view

Strugeon

Oliveira

Zekri

et al. 2021

The Knowledge Engineering Review

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Introduced as an interdisciplinary area that combines multi-agent systems, data mining and knowledge discovery, agent mining is currently in practice. To develop agent mining applications involves a combination of different approaches (model, architecture, technique and so on) from software agent and data mining (DM) areas. This paper presents an investigation of the approaches used in the agent mining systems by deeply analyzing 121 papers resulting from a systematic literature review. An ontology was defined to capitalize the knowledge collected from this study. The ontology is organized according to seven main facets: the problem addressed, the application domain, the agent-related and the mining-related elements, the models, processes and algorithms. This ontology is aimed at providing support to decisions about agent mining application design.

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Section: Introductionmentioning

confidence: 99%

Agent mining approaches: an ontological view

Strugeon

Oliveira

Zekri

et al. 2021

The Knowledge Engineering Review

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“…The use of data mining so far is commonly used to obtain optimal information from a large group of large databases that have complexity [5]. In a study of single Pap smear image classification found in the Herlev dataset [4], data mining was used to get information from 20 features in the data to identify pathologic cases of cervical cancer.…”

Section: Introductionmentioning

confidence: 99%

Improving Hierarchical Decision Approach for Single Image Classification of Pap Smear

Riana¹,

Ramdhani²,

Prasetio³

et al. 2018

IJECE

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The single image classification of Pap smears is an important part of the early detection of cervical cancer through Pap smear tests. Unfortunately, most classification processes still require accuracy enhancement, especially to complete the classification in seven classes and to get a qualified classification process. In addition, attempts to improve the single image classification of Pap smears were performed to be able to distinguish normal and abnormal cells. This study proposes a better approach by providing different handling of the initial data preparation process in the form of the distribution for training data and testing data so that it resulted in a new model of Hierarchial Decision Approach (HDA) which has the higher learning rate and momentum values in the proposed new model. This study evaluated 20 different features in hierarchical decision approach model based on Neural Network (NN) and genetic algorithm method for single image classification of Pap smear which resulted in classification experiment using value learning rate of 0.3 and momentum of 0.2 and value of learning rate of 0.5 and momentum of 0.5 by generating classification of 7 classes (Normal Intermediate, Normal Colummar, Mild (Light) Dyplasia, Moderate Dyplasia, Servere Dyplasia and Carcinoma In Situ) better. The accuracy value enhancemenet were also influenced by the application of Genetic Algorithm to feature selection. Thus, from the results of model testing, it can be concluded that the Hierarchical Decision Approach (HDA) method for Pap Smear image classification can be used as a reference for initial screening process to analyze Pap Smear image classification.

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“…However, when data are gathered at inconsistent intervals, the model must be adjusted accordingly. While this review does not delve into time-series pre-processing, the research by Pruengkarn et al [138] can be used as a resource for a thorough understanding of the matter.…”

mentioning

confidence: 99%

A Review of Time-Series Forecasting Algorithms for Industrial Manufacturing Systems

Fatima,

Rahimi

2024

Machines

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Time-series forecasting is crucial in the efficient operation and decision-making processes of various industrial systems. Accurately predicting future trends is essential for optimizing resources, production scheduling, and overall system performance. This comprehensive review examines time-series forecasting models and their applications across diverse industries. We discuss the fundamental principles, strengths, and weaknesses of traditional statistical methods such as Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing (ES), which are widely used due to their simplicity and interpretability. However, these models often struggle with the complex, non-linear, and high-dimensional data commonly found in industrial systems. To address these challenges, we explore Machine Learning techniques, including Support Vector Machine (SVM) and Artificial Neural Network (ANN). These models offer more flexibility and adaptability, often outperforming traditional statistical methods. Furthermore, we investigate the potential of hybrid models, which combine the strengths of different methods to achieve improved prediction performance. These hybrid models result in more accurate and robust forecasts. Finally, we discuss the potential of newly developed generative models such as Generative Adversarial Network (GAN) for time-series forecasting. This review emphasizes the importance of carefully selecting the appropriate model based on specific industry requirements, data characteristics, and forecasting objectives.

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A Review of Data Mining Techniques and Applications

Cited by 7 publications

References 143 publications

Agent mining approaches: an ontological view

Agent mining approaches: an ontological view

Improving Hierarchical Decision Approach for Single Image Classification of Pap Smear

A Review of Time-Series Forecasting Algorithms for Industrial Manufacturing Systems

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