Knowledge Discovery In Databases Research Articles

Fuzzy formal concept analysis: approaches, applications and issues

Formal concept analysis (FCA) is today regarded as a significant technique for knowledge extraction, representation, and analysis for applications in a variety of fields. Significant progress has been made in recent years to extend FCA theory to deal with uncertain and imperfect data. The computational complexity associated with the enormous number of formal concepts generated has been identified as an issue in various applications. In general, the generation of a concept lattice of sufficient complexity and size is one of the most fundamental challenges in FCA. The goal of this work is to provide an overview of research articles that assess and compare numerous fuzzy formal concept analysis techniques which have been suggested, as well as to explore the key techniques for reducing concept lattice size. as well as we'll present a review of research articles on using fuzzy formal concept analysis in ontology engineering, knowledge discovery in databases and data mining, and information retrieval.

Fuzzy formal concept analysis: approaches, applications and issues

Formal concept analysis (FCA) is today regarded as a significant technique for knowledge extraction, representation, and analysis for applications in a variety of fields. Significant progress has been made in recent years to extend FCA theory to deal with uncertain and imperfect data. The computational complexity associated with the enormous number of formal concepts generated has been identified as an issue in various applications. In general, the generation of a concept lattice of sufficient complexity and size is one of the most fundamental challenges in FCA. The goal of this work is to provide an overview of research articles that assess and compare numerous fuzzy formal concept analysis techniques which have been suggested, as well as to explore the key techniques for reducing concept lattice size. as well as we'll present a review of research articles on using fuzzy formal concept analysis in ontology engineering, knowledge discovery in databases and data mining, and information retrieval.

Host-based intrusion detection system using Support Vector Machine

In line with the communication industry’s use of recent advancements in network technology to link remote areas of the world, attackers or intruders have stepped up their attacks on networking infrastructure. System administrators might deploy intrusion detection tools and systems to thwart such efforts. In recent years, the use of machine learning (ML) techniques in intrusion detection systems (IDSs) has increased. One of the most popular machine learning (ML) techniques for intrusion detection is the Support Vector Machine (SVM) due to its excellent generalization and capacity to escape the dimensionality curse. Recent studies have shown that the number of dimensions still impacts how well SVM-based intrusion detection systems work. The fact that SVM assesses all data characteristics equally has also caused some concerns. Actual intrusion detection datasets include a lot of redundant or superfluous characteristics. It would be ideal to consider feature weights while training an SVM. Knowledge Discovery in Databases (KDD) intrusion detection dataset offers labeled data for the scientists and researchers; choosing the essential features or patterns from the input dataset makes the problem more straightforward and faster and acquires much more accuracy towards threat detection. Our work demonstrates the efficiency of recognizing the essential input patterns to design a more efficient Intrusion Detection System (IDS). Consequently, removing irrelevant or unimportant inputs makes the problem of detecting a threat simpler, faster, and more accurate. It has been an essential issue in intrusion detection that features selection and ranking must be made accordingly; it is the only way to detect intrusion accurately and efficiently. We implement the procedure to remove one feature at a time to run experiments on a Support Vector Machine (SVM) to grade the significance of the features for the KDD dataset. It has been observed that SVM-based IDSs utilizing fewer features could improve and efficiently perform.

Proposal of a method to classify female smokers based on data mining techniques

Smoking is considered the main cause of preventable deaths in the world. The level of cigarette consumption can be determined from several attributes such as psychological stress, nicotine dependence, and behavioral characteristics. This paper seeks to contribute to this research field by applying data mining techniques using a methodology based on the Knowledge Discovery in Databases (KDD) process to classify female smokers as “light smokers” or “heavy smokers”. The tests were conducted on four datasets extracted from the National Survey on Drug Use and Health (NSDUH, USA) database. Feature selection and ranking techniques were applied to reduce dimensionality and the resulting dataset was used as input for eight data mining techniques. The best results were obtained with Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Logistic Regression (LR), which had an accuracy of approximately 85% and recall of up to 77%. Precision values were approximately 81% and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) surpassed 91% in some tests. The features considered most relevant for both classes were identified and discussed. The CIGAGE variable, which is the age when a woman started smoking daily, for example, shows that each year that a woman lives without smoking on a daily basis reduces the chances of her being classified as a heavy smoker by a factor of 0.911. The findings in this paper enable an assessment of the impact of predictive features related to the consumption of industrialized cigarettes, indicating how public policies and financial resources should be applied to combat smoking among women.

Intrusion detection in healthcare domain using machine learning

Intrusion detection system is considered as one of the main sources for protection of important information and communication technologies especially in healthcare networks. Intrusion detection system must be updated frequently because of intrusions that improves periodically. Due to particular limitations such as resource-constrained devices, limited memory and battery capacity of nodes, and unique protocol stacks, traditional intrusion detection algorithms must be updated and improved for application to the Internet of Things. To address this issue, a lightweight attack detection approach based on supervised machine learning–based FIDS (Frothy Disturbance Intrusion Detection System) was developed to detect an adversary attempting to inject unneeded data into the network. FIDS can play an important role in intrusion detection. The proposed model was trained with KDD (Knowledge Discovery in Database) dataset using SVM (Support Vector Machine) algorithm. AODV (Ad-hoc- on-demand Distance Vector) routing protocol was used for routing to make it more energy efficient since the energy usable was limited. The process of packet transmission was done with the help of clustering algorithm to separate the sensors into groups with each group containing a cluster head. 

Implementasi Algoritma Apriori dan FP-Growth pada Penjualan Spareparts

Data Mining can be applied in various areas, for example in PT. Agung Toyota Denpasar in order to increase sales and determine the sale of replacement parts. The current problem is to determine the replacement parts sale in PT. Agung Toyota Denpasar cannot know the purchasing habits of customers or customers in purchasing replacement parts purchased simultaneously. This research aims to implement apriori algorithms and fp-growth algorithms to form a model or a combination of rules so that businesses can increase their sales. Using the Knowledge Discovery Database (KDD) method should provide significant information on transaction patterns purchased simultaneously using the apriori and fp-growth algorithms. The dataset used to support this research is the sales transactional dataset for the period of January 2022. The results showed that the 10 best association rules of apriori algorithms and fp-growth algorithms were ready to be used to increase sales with a minimum support value of 85%, confidence value of 100%, and the highest lift ratio of 2.03.

Characterizing Infant Mortality Using Data Mining - A Case Study in Two Brazilian States - Santa Catarina and Amapá.

Infant mortality is characterized by the death of young children under the age of one, and it is an issue affecting millions of children in the world. The objective of this article is to employ concepts of knowledge discovery in databases, specifically of machine learning in the data mining phase, to characterize infant mortality in two states of Brazil: Santa Catarina, with the lowest infant mortality rate of the country's states, and Amapá, with the highest. The classifiers C4.5, JRip, Random Forest, SVM, and Multilayer Perceptron were used, and a brief comparison of the results obtained by the classifiers in both states is made. In addition, the dataset preprocessing is detailed, which includes attribute selection and class balancing. The results show that the features APGAR5, WEIGHT, and CONGENITAL ANOMALY stood out the most from the rules generated by the tree-based classifiers.

Knowledge discovery in manufacturing datasets using data mining techniques to improve business performance

Recently due <span>to the explosion in the data field, there is a great interest in the data science areas such as big data, artificial intelligence, data mining, and machine learning. Knowledge gives control and power in numerous manufacturing areas. Companies, factories, and all organizations owners aim to benefit from their huge; recorded data that increases and expands very quickly to improve their business and improve the quality of their products. In this research paper, the knowledge discovery in databases (KDD) technique has been followed, “association rules” algorithms “Apriori algorithm”, and “chi-square automatic interaction detection (CHAID) analysis tree” have been applied on real datasets belonging to (Emisal factory). This factory annually loses tons of production due to the breakdowns that occur daily inside the factory, which leads to a loss of profit. After analyzing and understanding the factory product processes, we found some breakdowns occur a lot of days during the product lifecycle, these breakdowns affect badly on the production lifecycle which led to a decrease in sales. So, we have mined the data and used the mentioned methods above to build a predictive model that will predict the breakdown types and help the factory owner to manage the breakdowns risks by taking accurate actions before the breakdowns happen.</span>

Toddler Nutritional Status Classification Using C4.5 and Particle Swarm Optimization

Abstract. Purpose: This research was conducted to create a classification model in the form of the most optimal decision tree. Optimal in this case is the combination of parameters used that will produce the highest accuracy compared to other parameter combinations. From this best model, it will be used to predict the nutritional status class for the new data.Methods/Study design/approach: The dataset used is from Nutritional Status Monitoring in 2017 in Riau Province, Indonesia. From the dataset, the Knowledge Discovery in Database (KDD) stages were carried out to build several classification models in the form of decision trees. The decision tree that has the highest accuracy will then be selected to predict the class for the new data. Predictions for new data (unclassified data) will be made in a web-based system.Result/Findings: Particle Swarm Optimization is used to find optimal parameters. Before PSO is used, there are 213 parameters in the dataset that can be used to do classification. However, using many such parameters is time-consuming. After PSO is used, the optimal parameters found are the combination of 4 parameters, which can produce the most optimal decision tree. The 4 chosen parameters are gender, age (in months), height, and the way to measure the height (either stand up or lie down). The most optimal decision tree has an accuracy of 94.49%. From the most optimal decision tree, a web-based system was built to predict the class for new data (unclassified data).Novelty/Originality/Value: Particle Swarm Optimization (PSO) is a method that can help to select the most optimal parameters, or in other words produce the highest classification accuracy. The combination of parameters selected has also been confirmed by the nutritionist. The prediction system has been declared feasible to be used by nutritionists through the User Acceptance Test (UAT).

Predictive Analytics on Product Sales at Heva Inc. Using K – Means Method

Prediction is the process of estimating something that is most likely to happen in the future based on previous and current knowledge that is owned, with the goal of minimizing the error. Prediction allows people to recognize and then solve difficulties that are occurring or are expected to arise. This study began with preparation, literature review, data collection, and knowledge discovery in databases (KDD). One of the processes is data mining using the K – Means method, which is critical for obtaining the research's results and conclusions. This research also uses the RapidMiner application as a comparison of the results with the results obtained by python coding. By using 4 clusters, products were categorized into 4 labels, namely very good products, good products, bad products, and very bad products. The research resulted in 11 products in the bad product category, 12 products in the good product category, 10 products in the very good category, and 18 products in the very good product category. The very good product label was further clarified with visualization to show the best time to restock each recommended product.

Comparative analysis of forecasting models in healthcare (COVID-19)

Knowledge discovery in databases (KDD) is another name of Data mining. It is an interdisciplinary area which focuses on extraction of useful knowledge from data in every sector like health, education, business etc. There are many fields to explore like business, health care, e-commerce etc but nowadays, as covid pandemic is affecting everyone and due to surge in coronavirus cases causing shortage of hospital beds, oxygen supplies, vaccine and turning away patients from hospitals, put creaky health infrastructure in spotlight. The plenty of data is available in the medical field of these conditions. To analyse the problems, there are many data mining approaches which can be used to extract useful patterns from these types of data to follow the upcoming trends. This study is to compare the various models like KNN, improved RF model and multilayer perceptron by using SPSS and python software. The data of COVID-19 has been taken from Kaggle’s website which is based on the symptoms and the forecasted results has been shown.

KNOWLEDGE DISCOVERY AND DATA MINING OF STRUCTURED AND UNSTRUCTURED BUSINESS DATA: PROBLEMS AND PROSPECTS OF IMPLEMENTATION AND ADAPTATION IN CRISIS CONDITIONS

In modern conditions of the development of the global economy and in connection with the emergence of new branches of economic activity in the field of IT, the phenomenon of Structured and Unstructured Big Data - the use of Data Science for advanced in-depth analysis of data and knowledge in all possible modes - leads to competitive advantages for corporations and institutions, both at the regional and interstate levels, which is especially relevant in the context of the current macroeconomic and military crisis [1].The following topical issues are systematically investigated in the article: current status and prospects for further development of Knowledge Discovery in Data Base (KDD), problems and critical issues of theory and practice of Data Mining, the specifics of effective use of Knowledge Discovery in DB (Data Base) in the current crisis in Ukraine.The above trends and features of the KDD market should be taken into account in further theoretical research and practical implementation or reengineering of KDD systems in Ukraine. The obtained results are relevant and applicable not only for local companies and organizations, but also for international applications in the context of global, regional macroeconomic and current national crisis phenomena.

A Comprehensive Survey on Affinity Analysis, Bibliomining, and Technology Mining: Past, Present, and Future Research

Recent advancements in high-speed communications and high-capacity computing systems have contributed to major growth in the data volume of databases. Data mining is a crucial part of information retrieval; it is often termed as database knowledge discovery. It consists of techniques for examining massive data sets, to find hidden (but possibly important) information. Three interesting fields in data mining are affinity analysis, bibliomining, and technology mining. Affinity analysis provides data mining techniques to determine the similarity among objects; bibliomining is a combination of data mining, bibliometrics, and data warehousing; technology mining is a research topic that is an obstacle to many scientists in the fields of time association, enterprise association, and computer programming. We present a systematic review of the notable research articles in the fields of affinity analysis, bibliomining, and technology mining published between 2000 and December 2021. We provide a systematic analysis of the selected literature by specifying the major contributions, used data sets, performance evaluations, and limitations. Our findings demonstrate that affinity analysis interoperability extends well beyond market basket analysis. We also demonstrate that, in the age of big data, the personalized needs of users are the driving forces behind the evolution of the digital library from a resource-sharing service to a user-centered service. Finally, this article provides insight into major advances and outstanding challenges in the fields of affinity analysis, bibliomining, and technology mining.

Data Mining Prediction Techniques in Health Care Sector

Knowledge discovery in databases (KDD) is another name of Data mining. It is an interdisciplinary area which focuses on extraction of useful knowledge from data in every sector like health, education, business etc. Nowadays, as covid pandemic is affecting everyone and due to surge in coronavirus cases causing shortage of hospital beds, oxygen supplies, vaccine and turning away patients from hospitals, put creaky health infrastructure in spotlight. The plenty of data is available in the medical field of these conditions. Data mining approaches can be used to extract useful patterns from these types of data to follow the upcoming trends. This paper focuses on various data mining techniques which can be used in medical industries for the best outcome.

Penerapan Algoritma Rought Set Untuk Memprediksi Jumlah Permintaan Produk

Data Mining is a term used to describe the discovery of knowledge in databases. Data mining is a process that uses statistical, mathematical, artificial intelligence, and machine learning techniques to extract and identify useful information and tied knowledge from large databases. Demand is a number of goods purchased or requested at a certain price and time. Demand is related to consumer desires for goods and services to be fulfilled. And the tendency of consumer demand for goods and services is unlimited. According to the everyday sense of demand is defined in absolute terms, namely the number of goods needed. This way of thinking is based on the idea that humans have needs. For this need, the individual has a demand for goods, the more residents of a country, the greater the public's demand for goods. Rapid Miner is open source software. Rapid Miner is a solution for analyzing data mining, text mining and clustering analysis. Rapid Miner uses a variety of descriptive and predictive techniques to provide users with insights so they can make the best decisions

Sustainable Development Assessment of Cultural and Creative Industries in Casino Cities: A Case Study of Macao

In Macao, the government established the Cultural and Creative Industry Promotion Office and Cultural Industries Committee in 2010, which nominated eight to-be-developed cultural and creative industries (CCIs): design, visual arts, performing arts, clothing, pop music, film and video, animation, and publishing. However, because each CCI has its unique pattern and environmental resources are very limited in Macao, an industrial chain analysis for these eight industries should be conducted prior to policy implementation. Therefore, this study organized an industrial feasibility analysis for these eight CCIs. The methodologies included in-depth interviews, a literature analysis, and knowledge-discovery in databases. On the other hand, this study adopted the concept of creative industries, “the relationship between production and reproduction”, and “the three-circle hypothetical interactive consumption” model for positioning these eight CCIs to choose existing industries in Macao, such as the exhibition, gambling, and cultural tourism industries, that are likely to promote CCIs. Next, the orientations of these CCIs are determined. Finally, it is suggested that the performing arts, design, and visual arts industries should be prioritized currently, and the heritage management and digital media industries are advised as to-be-developed ones. In contrast, the clothing, pop music, film and video, animation, and publishing industries are not so beneficial for Macao’s development.

Analisis Sentimen Ulasan Aplikasi Mola Pada Google Play Store Menggunakan Algoritma Support Vector Machine

MOLA is one of the video streaming platform applications on the google play store. The mola application has been downloaded 5 million times but only has a 3.2 rating. On the Google Play Store app rating is followed by user reviews of the app. There are quite a lot of reviews that are unstructured and contain opinions from users about their satisfaction with the application so that it is often taken into consideration by potential users to choose the application used. Based on this, sentiment analysis was carried out using the Support Vector Machine algorithm to find out how the sentiments of users towards the MOLA application on the google play store were carried out. This study uses the Knowledge Discovery in Database (KDD) method. The data used is a review of the MOLA application with as many 520 data consisting of 312 positive reviews and 208 negative reviews. The best results are obtained in scenario 1 (90:10) using the RBF (Radial Basis Function) kernel which produces 92.31% accuracy, 96.3% precision, 89.66% recall, and 92.86% f1-score.
 
 Keywords: Sentiment Analysis, Support Vector Machine, MOLA

An enhanced classification framework for intrusions detection system using intelligent exoplanet atmospheric retrieval algorithm

Currently, many companies use data mining for various implementations. One form of implementation is intrusion detection system (IDS). In IDS, the main problem for nuisance network administrators in detecting attacks is false alerts. Regardless of the methods implemented by this system, eliminating false alerts is still a huge problem. To describe data traffic passing through the network, a database of the network security layer (NSL) knowledge discovery in database (KDD) dataset is used. The massive traffic of data sent over the network contains excessive and duplicated amounts of information. This causes the classifier to be biased, reduce classification accuracy, and increase false alert. To that end, we proposed a model that significantly improve the accuracy of the intrusion detection system by eliminating false alerts, whether they are false negative or false positive negative alerts. The results show that the proposed intelligent exoplanet atmospheric retrieval (INARA) algorithm has improved accuracy and is able to detect new attack types efficiently.

Implementasi Algoritma K-Nearest Neighbor dalam Menentukan Buku Berdasarkan Peminatan

The use of computer technology helps a lot in human performance in various data and information management. Therefore, researchers use computers to make analyzes that can predict favorite books based on data from book borrowing records in the library. Researchers use is data mining. Data mining is a term used to describe the discovery of knowledge in databases, using statistical techniques, mathematics, artificial intelligence, and machine learning to extract and identify information that is useful for science. The use of data mining requires a method that can manage book borrowing data so that it gets the predictions of favorite books. The method used is K-Nearest Neighbor (KNN). The results of the accuracy in this study are 98.75%, Prediction of Disinterest with true Not Interest is 28 data, Prediction of Disinterest with true Interest is 1 data, Prediction of Interest with true No Interest is 0 data, Interest Prediction with true Interest is 54 data.

Intelligent Governance of Network Popular Opinion Based on DM Technology in the New Media Age

The public’s desire to protect their own interests has increased in recent years. As information technology advances, the number of Internet users grows, the penetration rate improves, and the number of self-media users of network new media grows. Using historical data to predict future behavior and learn from massive amounts of data, humanity has entered the new media age, thanks to the rapid advancement of mobile Internet technology. It solves the problem that traditional database management systems cannot find hidden relationships and rules in data and cannot predict the future based on the data they have. This paper investigates the use of DM technology to intelligently govern network public opinion. Finance, telecommunications, network-related industries, retailers, manufacturing, medical care, pharmaceutical industry, and scientific fields have all made extensive use of it. Data mining (abbreviated as DM) is a type of data analysis that extracts credible, novel, effective, and understandable patterns from large amounts of data. It is closely related to database knowledge discovery.