Journal of Data Mining and Knowledge Engineering ISSN: 3139-5341 (Online)

Abstract

The Semantic Web aims to create a more intelligent web through the use of data that is understandable by machines, thus enabling the development of more powerful applications. In this context, ontology-based knowledge representation plays a crucial role in structuring and categorizing data in ways that enhance the effectiveness of data mining processes. This paper explores the concept of ontology-based knowledge representation in data mining, specifically in the Semantic Web, and its role in making data more interpretable, accessible, and usable. The paper examines various ontology models, knowledge representation frameworks, and how they interact with data mining techniques. It also highlights the importance of ontologies in ensuring consistency and semantic clarity, which is essential for the success of intelligent web-based applications. Additionally, the paper presents a set of use cases demonstrating the integration of ontology-based knowledge in data mining, followed by a discussion on the challenges and future directions for research in this area.

Keywords: Ontology, Knowledge Representation, Data Mining, Semantic Web, Semantic Technology, Intelligent Web, Ontology Models

Abstract

Data mining has become a cornerstone in healthcare, enabling the extraction of actionable insights from vast amounts of patient data. Predictive modeling techniques are particularly valuable in optimizing patient care by forecasting outcomes, improving decision-making, and enhancing healthcare delivery. This paper explores the integration of data mining methods in healthcare, focusing on predictive modeling applications such as disease prediction, patient risk assessment, and treatment optimization. Through an examination of various algorithms and their healthcare applications, this paper aims to demonstrate how predictive models can be leveraged to optimize patient care, reduce costs, and improve overall healthcare outcomes. Additionally, it investigates the challenges and ethical considerations involved in implementing these models in clinical settings.

Keywords: Data mining, predictive modeling, healthcare, patient care optimization, disease prediction, risk assessment, treatment optimization, machine learning, healthcare analytics.

Abstract

The proliferation of digital content has created an urgent need for effective tools to process, analyze, and extract meaningful insights from vast unstructured textual data. Machine learning (ML) algorithms have emerged as a pivotal force in text mining, enabling knowledge discovery across diverse domains such as healthcare, education, finance, and social media analysis. This paper explores the applications of ML in text mining for knowledge discovery, focusing on techniques like natural language processing (NLP), deep learning, and unsupervised learning. Through the integration of original tables and 2D figures, the study delves into the methodologies, challenges, and future directions in this dynamic field.

Keywords: Machine Learning, Text Mining, Knowledge Discovery, Natural Language Processing, Deep Learning, Unsupervised Learning

Abstract

Knowledge engineering plays a pivotal role in the development and enhancement of expert systems, facilitating automated reasoning and intelligent decision-making. This paper delves into the challenges faced by knowledge engineering, including knowledge acquisition, representation, and validation, in designing robust expert systems. Furthermore, emerging trends such as explainable AI, integration with big data, and advancements in natural language processing (NLP) are explored, illustrating their potential to shape the future of expert systems. The discussion is supported by original tables and figures for better comprehension.

Keywords: Knowledge Engineering, Expert Systems, Artificial Intelligence, Challenges, Future Trends, Explainable AI, Natural Language Processing, Big DATA

Abstract

Big Data has transformed the decision-making landscape in business intelligence (BI), offering organizations the ability to process vast volumes of data for actionable insights. This paper explores advanced data mining techniques such as clustering, association rule mining, and deep learning, emphasizing their integration into BI frameworks. It examines how these methods facilitate predictive analytics, improve customer segmentation, and optimize resource allocation. The challenges of Big Data, including data quality and computational complexity, are discussed alongside strategies to overcome them. Case studies and visualizations illustrate practical applications, demonstrating the profound impact of Big Data mining on modern BI.

Keywords: Big Data, Business Intelligence, Data Mining, Predictive Analytics, Clustering, Decision-Making, Deep Learning, Resource Optimization

Abstract

Association rule mining, a fundamental aspect of data mining, offers significant potential for optimizing knowledge engineering processes. This paper examines the implementation of association rule mining algorithms, such as Apriori and FP-Growth, within knowledge engineering systems to enhance knowledge discovery and decision support. We analyze the effectiveness of these algorithms in identifying relationships and patterns within large datasets, thereby facilitating more informed decision-making. Through case studies and empirical analysis, we illustrate how association rule mining can be leveraged to optimize knowledge management practices, providing valuable insights and improving overall system performance.

Keywords: Association Rule Mining, Knowledge Engineering, Apriori, FP Growth, Decision Support

Abstract

Clustering techniques play a crucial role in the field of data mining, offering significant benefits for knowledge engineering. This paper explores the application of various clustering algorithms, including k-means, hierarchical clustering, and DBSCAN, to improve knowledge organization and retrieval. We present a detailed analysis of the strengths and weaknesses of these algorithms in different scenarios, emphasizing their practical implementation in real-world knowledge engineering systems. Through experimental studies and practical examples, we showcase how clustering techniques can facilitate the efficient categorization and management of large datasets, ultimately enhancing the overall knowledge engineering process.

Keywords: Clustering, Knowledge Engineering, K-means, Hierarchical Clustering, DBSCAN

Abstract

The field of knowledge engineering has been significantly enhanced by the advent of predictive data mining algorithms. This paper investigates the application of various predictive models, such as decision trees, neural networks, and support vector machines, within knowledge engineering systems. Our research focuses on the implementation and optimization of these algorithms to improve knowledge discovery and extraction processes. By analyzing large datasets across different domains, we demonstrate the efficacy of predictive data mining in uncovering patterns and insights that are critical for strategic decision-making. The study provides a comprehensive overview of the methodologies and their impact on the evolution of knowledge engineering practices.

Keywords: Predictive Data Mining, Knowledge Engineering, Decision Trees, Neural Networks, Support Vector Machines

Abstract

Data mining, a pivotal element of knowledge engineering, has undergone significant transformations with the advent of advanced computational techniques. This paper delves into the integration of contemporary data mining methodologies within knowledge engineering frameworks. Emphasizing the synergy between data mining and knowledge management, we explore various algorithms, including clustering, classification, and association rule mining, to enhance decision-making processes. By leveraging big data and machine learning, we demonstrate how these systems can evolve to meet the growing demands of complex data environments. Through comprehensive case studies and practical implementations, we illustrate the transformative potential of advanced data mining techniques in fostering innovative knowledge engineering solutions.

Keywords: Data Mining, Knowledge Engineering, Machine Learning, Big Data, Decision-Making

Abstract

The idea behind Big Data includes analysing capacious data to extract important information. With the help of data science the fashion and textile industry can forward due to its beauty. Huge amount of data can be produced in the Internet of Things age from number of heterogeneous sources like mobile devices, sensors, and social media. A new approach consisting of the integration of two technologies is block chain and big data. The purpose of application of big data analytics is some cases are for decision making about the new technologies in textile in the developing world. Nowadays, the number of studies applying big data technology in the textile and manufacturing sector for decision making is limited. It is an important tool in forecasting fashion trends. Big Data information can be utilized for pattern investigation, clients’ conduct examination, estimating and many more. The fashion industry is gaining lot of changes in way designers produce and promote their items as a result of the emergence of big-data.

Keywords: Methodology, data analytics, data science, e-commerce, block chain, forecasting trends, design house, client budgets.

Abstract

Cross-domain data mining has gained prominence as a crucial research area in machine learning and data mining, focusing on leveraging knowledge acquired in one domain to improve performance in another. This paper explores the concepts of knowledge transfer and adaptation in the context of cross-domain data mining. We present an overview of the challenges, methodologies, and applications related to transferring and adapting knowledge across diverse domains. The paper also includes illustrative tables to enhance understanding and provide a comprehensive reference for researchers and practitioners.

Keywords- Cross-Domain Data Mining, Knowledge Transfer, Adaptation, Machine Learning, Data Mining, Transfer Learning, Domain Adaptation, Domain Discrepancy, Labeling Heterogeneity, Distribution Shift, Feature Variability.

Abstract

Privacy-preserving data mining (PPDM) has become increasingly crucial in the era of big data, where vast amounts of sensitive information are collected and analyzed for various purposes. This paper presents a comparative analysis of popular privacy preserving data mining techniques, evaluating their strengths and weaknesses in preserving individual privacy while extracting meaningful patterns from data. The techniques under scrutiny include homomorphic encryption, differential privacy, secure multiparty computation, and federated learning. We provide a comprehensive overview of each technique, analyze their applicability in different scenarios, and present a set of comparative tables to aid researchers and practitioners in selecting the most suitable method for their specific use case.

Keywords- Privacy-preserving data mining, Comparative analysis, Homomorphic encryption, Differential privacy, Secure multiparty computation, Federated learning, Computational overhead, Privacy guarantees, Data utility, Scalability.

Abstract

Text mining, a subfield of natural language processing, plays a crucial role in extracting valuable information from vast amounts of unstructured textual data. In this paper, we present a case study on applying text mining techniques to extract meaningful information from news articles. The study involves the application of various text mining tools and methodologies, including tokenization, named entity recognition, sentiment analysis, and topic modeling. We also explore the integration of machine learning algorithms to enhance the accuracy and efficiency of information extraction.

Keywords- Text Mining, Information Extraction, Natural Language Processing, News Articles, Named Entity Recognition, Sentiment Analysis, Topic Modeling, Machine Learning, Data Preprocessing, Textual Data, Information Retrieval, NLP, Text Analytics.

Abstract

This paper explores the dynamic field of social media data mining with a focus on sentiment analysis and trend prediction. With the increasing prevalence of social media platforms, the wealth of user-generated content presents a valuable source of information for understanding public sentiment and predicting emerging trends. The paper investigates various data mining techniques applied to social media data and provides insights into their applications, challenges, and potential advancements.

Keywords- Social media data mining, Sentiment analysis, Trend prediction, Machine learning, Natural language processing, Deep learning, Time-series analysis, Clustering algorithms, Data noise, Bias in data, Explainable AI.

Abstract

Financial fraud poses a significant threat to the stability and integrity of financial systems worldwide. Detecting fraudulent activities in financial transactions has become a critical challenge for financial institutions. This paper explores the application of data mining techniques for fraud detection in financial transactions. The objective is to leverage advanced analytics and machine learning algorithms to enhance the accuracy and efficiency of fraud detection systems. The study employs a comprehensive dataset to train and evaluate various data mining models, presenting results through tables to highlight the effectiveness of the proposed approach.

Keywords- Fraud Detection, Data Mining, Financial Transactions, Machine Learning, Decision Trees, Neural Networks, Ensemble Methods, Preprocessing, Model Evaluation, Precision-Recall Trade-off.

Abstract

Knowledge mining systems are increasingly used to extract actionable insights from large-scale heterogeneous data. However, the reliability of mined knowledge is often questioned due to issues such as data noise, bias, incomplete context, and lack of traceability. Provenance tracking has emerged as a key mechanism to enhance trustworthiness by recording the origin, transformation history, and processing lineage of data and derived knowledge. This paper presents a comprehensive review of trustworthy knowledge mining with provenance tracking, covering conceptual foundations, provenance models, integration architectures, algorithms, and evaluation metrics. We discuss how provenance enables transparency, reproducibility, and accountability in knowledge discovery workflows. The study also examines applications in healthcare, finance, scientific data management, and knowledge graphs. Challenges such as scalability, privacy, and standardization are highlighted, and future research directions are proposed. The paper aims to provide a structured understanding of how provenance-aware mining frameworks can support trustworthy and explainable knowledge extraction in modern data-driven systems.

Keywords: Trustworthy AI, Knowledge Mining, Data Provenance, Explainability, Knowledge Graphs, Data Lineage, Reproducibility, Provenance Models

Abstract

Temporal knowledge graphs (TKGs) extend conventional knowledge graphs (KGs) by incorporating temporal information to represent dynamic relationships over time. Event evolution mining leverages TKGs to uncover temporal patterns, causality, and sequence of events in complex systems. With applications spanning social media analysis, finance, cybersecurity, and healthcare, TKGs and event evolution mining have emerged as crucial tools for understanding evolving phenomena. This paper presents a comprehensive review of TKGs, their construction, embedding techniques, temporal reasoning models, and event evolution mining methods. We discuss challenges such as data sparsity, reasoning over long-term dependencies, and scalability, alongside current solutions. Future research directions are highlighted to address limitations and enhance the utility of temporal knowledge systems.

Keywords: Temporal knowledge graphs, event evolution, temporal reasoning, dynamic knowledge graphs, temporal embeddings, causal inference, time aware graph modeling.

Abstract

The exponential growth of data across multiple domains has necessitated efficient integration methods to unify heterogeneous data sources. Semantic data integration (SDI) leverages ontology-driven frameworks, knowledge graphs, and semantic web technologies to provide a unified, meaningful representation of diverse datasets. Unlike traditional data integration techniques, SDI addresses semantic heterogeneity, structural differences, and inconsistencies by capturing the meaning of data rather than relying solely on schema matching. This review examines contemporary approaches to semantic data integration, including ontology-based mapping, RDF and SPARQL utilization, knowledge graph construction, and hybrid techniques. Challenges such as data quality, scalability, and dynamic source adaptation are discussed. The paper also presents comparative analyses, case studies, and implementation frameworks, highlighting trends and future directions in SDI research.

Keywords: Semantic data integration, heterogeneous data sources, ontology-based mapping, knowledge graphs, RDF, SPARQL, semantic web, data interoperability

Abstract

As one of the most prosperous applications of text analysis and understanding emotions and short messaging text apperception has recently received consequential attention especially during the past several years. This is corroborated by the blogs, chats, online reviews, discussions and systematic evaluations of text analysis techniques. There are 5 major aspects for this trend, first is the commercial and social marketing applications, second is the understanding of one‘s feelings, third human-computer interaction , fourth text to speech generation and fifth is the natural language and machine learning approaches and technologies after 20 years of research. This model includes happy or joy, sad, disgust, anger, fear and surprise as basic emotion. In this paper emotion detection using semantic approaches and rule based approaches are described. Keywords: NLP (Natural Language Processing),

Keyword based detection, Text Processing, Sentimental measures, Semantic approach, Parts of Speech (POS).

Abstract

This paper further enhances the techniques and chronological methods to perform the corresponding manipulation and further prediction analysis. We have acquired a real-time dataset based on the Twitter user's comments sections. The uniqueness of the dataset is that we have extracted only the particular comments which syntheses a particular word based on the product. Then further repetitive extraction is made to complete the dataset. Our dataset has three columns based on the ideology that the particular user or our focused subject has enhanced any detail about the product that we are observing. In this precise dataset, we have taken the subject about the usage of the product by the users that have been manufactured by the companies Google and Apple. Both technology giants have well versed their technology reign in this era, and they are further focused on their upcoming cyber projects, and their products will be more advanced in the future. As they are involved in further optimization in their devices and increasing their specifications. It would be a complex task to accomplish their project without the feedback, pros, and cons of their predecessor projects. They can extract such features from the Twitter API dataset, and they could further enhance their product. They could analyze the drawback, whether the product has reached the market and came out with success all type of this information can be extracted from social media instead of conducting a survey. Such a process would be a more hectic classification, and we cannot predict any accurate results. So we proceed with the social media Data mining Process.

Keywords: - Twitter Data, Dataset, Data Mining, API, Analyze.

Abstract

The technique of discovering or "mining" new knowledge from huge datasets and applying it to decision making is known as data mining (DM). Companies have recently used a variety of data mining approaches to better understand their consumers and their performance, as well as to address complicated business challenges. Data mining is a method of developing business intelligence (BI) using data collected, organised, and stored by an organisation. The goal of this study is to examine data mining as a technology that enables business intelligence. The study's data collection technique was document analysis, while the data analysis method was qualitative.

Keywords: Frequent Item Mining, Data Mining, Market Basket Analysis, Parallel Execution

Abstract

Data mining plays an important role in a variety of applications, including corporate organisations, educational institutions, and government sectors, as well as the health care industry, scientific and engineering fields. Data mining is mostly employed in the healthcare business for illness prediction. Chronic renal disease is one of the leading causes of mortality in India today. Data mining classifiers are used for prediction and may also be employed in the health field, where enormous amounts of data are created. Data mining techniques are being utilised in the healthcare industry to predict renal disease from data sets. So the many DM methodologies utilised to determine the accuracy for kidney-related disorders.

Keywords: DM methodologies, Classification algorithms, Data mining, Kidney disease