DATA MINING IEEE PAPERS AND PROJECTS-2020



Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems

Data stream mining , as its name suggests, is connected with two basic fields of computer science, ie data mining and data streams. Data mining [1 4] is an interdisciplinary subfield of computer science whose main aim is to develop tools and methods for exploring

In any data science and analytics project, the task of mapping a domain-specific problem to an adequate set of data mining methods by experts of the field is a crucial step. However, these experts are not always available and data mining novices be required to perform Data stream mining as its name suggests, is connected with two basic fields of computer science, ie data mining and data streams. Data mining [1 4] is an interdisciplinary subfield of computer science whose main aim is to develop tools and methods for exploring Ontology matching aims at identifying the correspondences between instances and data properties of different ontologies. The use of data mining approach in matching ontology problem is reviewed in this article. We propose DMOM ( Data Mining for Ontology Matching In ubiquitous stream of data the issue related to the association rules of fuzzy are considered in this paper, and a new method FFP_USTREAM (Fuzzy Frequent Pattern Ubiquitous Streams) are created. The system of Ubiquitous real-time data incorporates fuzzy Educational data mining is used to discover significant phenomena and resolve educational issues occurring in the context of teaching and learning. This study provides a systematic literature review of educational data mining in mathematics and science education. A total of

Better software analytics via DUO: Data mining algorithms using/used-by optimizers.
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This paper claims that a new field of empirical software engineering research and practice is emerging: data mining using/used-by optimizers for empirical studies, or DUO. For example, data miners can generate models that are explored by optimizers. Also, optimizers can

Multi-models of Educational Data Mining for Predicting Student Performance in Mathematics: A Case Study on High Schools in Cambodia
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Education is crucial for the development of any country. Analysis of education datasets requires effective algorithms to extract hidden information and gain the fruitful results to improve academic performance. Multiple models were used to maximize the contribution to Food safety is of vital interest for public health and the stability of society. In this paper, we analyzed the characteristics of food safety incidents (FSIs), including spatial distribution, food categories, risk factors, and supply chain links, reported by mainstream media in China

The use of Data Mining for Predicting Injuries in Professional Football Players
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Injuries in professional football represent a financial burden to clubs and have a negative impact on team performance. This has inspired a wide range of studies which attempt to gain insight into the risk factors associated with injury. Until recently, the majority of research Water is fundamental to human well-being, social development and the environment. Water development, particularly hydropower, provides an important source of renewable energy. Water development is strongly affected by poverty, but only few attempts have been made to Spectral classification and particular spectra identification are primary tasks for celestial object study in astronomy. With the developing of large spectrographic surveys, huge volumes of spectral data can be easily obtained and various data mining methods have

Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review
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Coronaviruses (CoVs) are a large family of viruses that are common in many animal species, including camels, cattle, cats and bats. Animal CoVs, such as Middle East respiratory syndrome-CoV, severe acute respiratory syndrome (SARS)-CoV, and the new

Can Data Mining Help Eddy Covariance See the Landscape A Large-Eddy Simulation Study
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Eddy-covariance fluxes serve as an essential benchmark for Earth system models and remote sensing data . However, two challenges prevent model- data intercomparisons from fully utilizing eddy-covariance fluxes. The first challenge stems from the differing and

Sentimental classification analysis of polarity multi-view textual data using data mining techniques.
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The data and information available in most community environments is complex in nature. Sentimental data resources possibly consist of textual data collected from multiple information sources with different representations and usually handled by different analytical Flood is one of the important destructive natural disasters in the world. Therefore, preparing flood susceptibility map is necessary for flood management and mitigation in a region. This research was planned to compare the performance of frequency ratio (FR), adaptive neuro Clustering is an important data mining technique. Due to privacy concerns associated with data mining privacy preservation algorithms have been developed in the last decade. Noise addition is a popular privacy preservation technique. However, in recent years a lot ofFinding frequent itemsets in a continuous streaming data is an important data mining task which is widely used in network monitoring, Internet of Things data analysis and so on. In the era of big data it is necessary to develop a distributed frequent itemset mining algorithm to In this analysis of the risk and return of stocks in global markets, we build a reasonably large number of stock selection models and create optimized portfolios to outperform a global benchmark. We apply robust regression techniques, LAR regression, and LASSO Method This was a retrospective descriptive study on the trajectories of care within the first year after bariatric surgery. Data were extracted from a national administrative claims database (the PMSI database) and trajectories were defined as principal diagnosis of Abstract Heating, Ventilation, and Air-Conditioning (HVAC) control strategies are set arbitrarily in many commercial buildings by operators, who sometimes lack relevant skills and professional training. It is acknowledged that improving the control strategy of HVAC is The economics of a mineral processing circuit is dependent on the numerous sensors that are critical to optimization and control systems. The sheer volume of sensors results in mines not being able to keep the sensors calibrated. When a sensor goes off calibration, it resultsAbstract Among business ethicists, Adam Smith is widely viewed as the defender of an amoral if not anti-moral economics in which individuals pursuit of their private self-interest is converted by an invisible handinto shared economic prosperity. This is often justified by Space technology has become an integral part of critical infrastructures and key elements for great power. Space telemetry data contain a wealth of information about the system behavior of artificial satellites. Recent developments in data mining techniques for anomaly The goal of spatio-temporal data mining is to discover previously unknown but useful patterns from the spatial and temporal data . However, explosive growth of the spatiotemporal data emphasizes the need for developing novel computationally efficientData mining is an approach through which in an synchronized manner we can find a workable solution that will be beneficial to increase the growth. The Farmers in agriculture sectors face a lot of issues and difficulties due to the improper understanding and

Towards Fair Educational Data Mining : A Case Study on Detecting At-risk Students
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Over the past decade, machine learning has become an integral part of educational technologies. With more and more applications such as students performance prediction, course recommendation, dropout prediction and knowledge tracing relying upon machine

Is the superior performance of optimized strategies caused by data mining bias
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There has been a long-standing debate on whether optimized strategies can consistently outperform the naive diversification strategy with statistical significance, initiated by DeMiguel, Garlappi, and Uppal (2009). However, few of the papers contributing to this

Prediction of Wheat Rust Diseases Using Data Mining Application
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Stem rust caused by Puccinia graminis f. sp. tritici and stripe rust caused by Puccinia striiformis are the most destructive wheat rust diseases when environment conditions are favorable in regions where wheat crops are grown. An early prediction mechanism can play

ROLE OF DATA MINING IN MALWARE DETECTION
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Data mining is the process of identifying patterns in large datasets. Data mining techniques are heavily used in scientific research (in order to process large amounts of raw scientific data ) as well as in business, mostly to gather statistics and valuable information to enhance

Text data mining using conversation analysis
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This paper suggests an algorithm of text data mining based on conversation analysis. Natural languages are developing dynamically nowadays. New semantic units are constantly being introduced into the spoken language. In these conditions, chains of Privacy preservation in Data Mining has become more prominent and popular because of its property of maintaining privacy of sensitive data for analysis purposes. In this decade, enormous volume of data is created by many sectors especially healthcare, and it is vital toOsteoporosis and its consequence of fragility fracture represent a major public health problem. Human exposure to heavy metals has received considerable attention over the last decades. However, little is known about the influence of co-exposure to multiple heavy The use of multi-dimensional indexing structures has gained a lot of attention in data mining . The most commonly used data structures for indexing data are R-tree and its variants, quad- tree, kd-tree, etc. These data structures support region queries (point, window and How can we extract meaningful knowledge from massive amounts of data The data mining group at University of Vienna contributes novel methods for exploratory data analysis. Our main research focus is on unsupervised learning, where we want to identify any kind of non This paper suggests a novel data mining algorithm for the evaluation of e-learning courses from a Learning Management System. This new algorithm, which is called S- Algo+(Superposition Algorithm), takes as input the course rankings and the suggestion

A Review on Data Mining Techniques and Challenges in Medical Field
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The healthcare industry has witnessed an enormous evolution in producing huge amounts of medical data that have given rise to research in multiple areas. Many researchers reviewed and surveyed the healthcare, which is an active interdisciplinary field of data

Patterns via Clustering as a Data Mining Tool
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Research shows that pattern structures are a useful tool for analyzing complex data . In our paper, we present a new general framework of using pattern structures as a data mining tool and as an application of the new framework we show a way to handle a classification

Choosing the best embryogenesis medium in carrot by data mining technology
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Plant cell, tissue and organ culture (PCTOC) is extensively used to propagate faster and more plants, to produce virus-free plants, and secondary metabolites production as well. This requires the optimization of PCTOC conditions for each plant and nal aim. Optimizing

Use of Internet of Things in Data Mining Models
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The Internet of Things is a new revolution as well as a technique that is evolving at a high rate at the moment. With the IoT, it is possible to attach all the gadgets which are used on the Web in daily routines. The IOT will have a huge impact on the development of peoples life

Analysis of Factors Effecting PISA Mathematics Literacy via Educational Data Mining
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Programme for International Student Assessment PISA, financed by Organization for Economic Co-operation and Development (OECD), is a large scale research targeting to collect information about Mathematics, Science literacy and reading skills of students of 15 Ocean mines are the major threat to the safety of great vessels and other living beings in the marine life. It is a self-contained explosive device placed in water to destroy ships or submarines. Due to various factors like variations in operating and target shapes

Diagnosis of Paddy Diseases using Data Mining Technique
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Day by Day the population is increasing and there is an urgent need to produce more food, especially in Asia. According to India today survey the farmers suicides are reduced but not eradicated. The main reason for Agriculture is the most vital part of Indias economy as 70

Efficient Distribution and Processing of Data for Parallelizing Data Mining in Mobile Clouds.
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We study different kinds of data distributions for improving the efficient, parallelized implementation of data mining in mobile cloud systems. Our formally-based approach ensures the correctness of the obtained parallel implementation. We apply our approach to

Data mining approach and security over Ddos attacks
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The benefit of on-demand services is one of the most important benefits of using cloud computing; therefore, the payment method in the cloud environment is pay per use. This feature results in a new type of DDOS attack called Economic Denial of Sustainability

JOB REQUIREMENTS ANALYSIS WITH DATA MINING TECHNIQUES
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Because a large number of job vacancies exist, nowadays, skills research has become the focus of the companies. This study presents a method to identify and analyze the most important requirements for a developer on the Romanian market. The skills needs is aFirstly, this paper introduces the types of clustering algorithm, and introduces the classical K- means algorithm and canopy algorithm in detail. Then, combining the map reduce computing model and spark cloud computing framework, this paper introduces the parallel

PROTECTION PRESERVING DATA MINING (PPDM) METHOD FOR CROSS DIVIDED DATA
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For finding concealed data from enormous database information mining is the best system to be utilized. For discovering connection betweens things or thing sets in an effective way, affiliation rule is found to be best among many mining rules additionally more analysts focus

Data mining classification algorithms
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Data mining nowadays became more and more important; it is used to extract knowledge from a large amount of data to help decision makers to make good decisions. Based on the type of the data and the type of the variable we want to predict, we choose the suitable

USING SOCIAL MEDIA DATA MINING TO UNDERSTAND THE PUBLIC PERCEPTION OF COAL IN THE UNITED STATES
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Jie Shi Liew, for the Master of Science degree in Geography and Environmental Resources, presented on June 2 20 at Southern Illinois University Carbondale. TITLE: USING SOCIAL MEDIA DATA MINING TO UNDERSTAND THE PUBLIC PERCEPTION OF COAL IN

Diagnosis of Various Thyroid Ailments using Data Mining Classification Techniques
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Classification is one of the most considerable supervised learning data mining technique used to classify predefined data sets the classification is mainly used in healthcare sectors for making decisions, diagnosis system and giving better treatment to the patients. In thisAbstract Building the Guangdong Hong Kong Macao Greater Bay Area is conducive to promoting the high economic integration of Guangdong, Hong Kong and Macao, building a new open economic system, and carrying out dual exploration of politics and economy A voluminous amount of data regarding users location services is being generated and shared every second. The anonymization plays a major role in data sanitization before sharing it to the third party by removing directly linked personal identifiers of an individual

Research on wireless distributed financial risk data stream mining based on dual privacy protection
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With the advancement of network technology and large-scale computing, distributed data streams have been widely used in the application of financial risk analysis. However, while data mining reveals financial models, it also increasingly poses a threat to privacy

INTRODUCTION TO DATA MINING AND PROCESS LEARNING TECHNIQUES
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The data mining is defined as a process used to extract usable data from a larger set of any raw data . It implies analysing data patterns in large batches of data using one or more software. Data mining has applications in multiple fields, like science and research. As an

Breast cancer prediction using Data mining
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Data mining is a powerful technology that can be used in all domains in order to detect hidden patterns from a large volume of data . A huge amount of medical data gives opportunities to health research community to extract new knowledge in different parts of

A SURVEY ON USE OF DATA MINING TECHNIQUE IN DIFFERENT DOMAIN
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Data mining is a process which finds useful patterns from large amount of data . Data mining is a knowledge discovery that extracts useful information. It has been a major advance in machine learning artificial agent systems and decision making in the expert systems. The

Calculating Diagnose Odd Ratio for Thyroid Patients using Different Data Mining Classifiers and Ensemble Techniques
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Thyroid hormones disorder is very common among people now-a-days. Various data mining techniques are used to identify thyroid problems. Machine learning provides help in multiple ways by different algorithms which are analyzed dataset and also generate different

AN ENHANCED OPTIMIZATION APPROACH FOR IMPROVING CLASSIFICATION ACCURACY IN DATA MINING
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This work presents an enhanced version of JAYA minimization algorithm to solve the issues in data mining classification approached with different objective functions that reflect in accuracy, reliability, cost, and efficiency improvement. The proposed enhanced JAYA

Clinical data mining on network of symptom and index and correlation of tongue-pulse data in fatigue population
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Background Fatigue is a kind of non-speci c symptom, which is widely found in sub-health and various diseases. It is closely related to peoples physical and mental health. Due to lack of objective evidence, it is often neglected in clinical diagnosis, especially in the early stage

DATA MINING CLASSIFICATION FOR BREAST CANCER PREDICTION
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Many machine learning methods are used in constructing a predictive model. In this study, a study was conducted on the performance of the Bagging, IBk and Random forest classification algorithms using the same dataset to predict the diagnosis of breast cancer

Data mining of structural, genomic and proteomic annotations for biological macromolecules
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Bioinformatics, understood as the application of computational tools on molecular data has become a very important field. The evolution of the biology and the technology has supposed an exponential growth of the quantity of available data about genome and protein

Educational Data Mining and Learning Analytics: Past, Present and Future
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Educational data mining (EDM) and learning analytics (LA) proved to be two notable themes in the field of education/teaching technologies in the last 10 years. Before elaborating on these two themes, it is worth identifying the difference between the concepts of analysis and

Data Mining and Analysis for Data From Vehicles Based on the OBDII Standard
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Today every new car has an OBDII (On Board Diagnostic II) port that can be used to retrieve vehicle diagnostic data using an ELM327 or STN1110 chip. This microcontroller can be used to determine the currently measured parameters of the vehicle, such as speed, engine

Data Mining Calibration Points from Oilfield Documents
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Abstract Daily Drilling Reports where drilling activities are entered as free text contain a wealth of information which is underutilised. Manual screening of Daily Drilling Reports (DDRs) is a tedious time-consuming exercise undertaken by engineers where only one

Decision support system for multistore online sales based on priority rules and data mining
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The work is focused on the design and implantation of an intelligent multi store E-commerce platform able to manage the orders and the warehouse stock by mean of priority association rules and data mining algorithms. The proposed Decision Support System (DSS) is

Research on Distributed Data Stream Mining of Financial Risk Based on Double Privacy Protection
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With the advancement of network technology and large-scale computing, distributed data streams have been widely used in the application of financial risk analysis. However, while data mining reveals financial models, it also increasingly poses a threat to privacy

Detection of Chronic Kidney Disease Stages by Data Mining Methods
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Data mining has created a new perspective in the use of health data in addition to finding answers to problems related to large amounts of data and it has become a method that the usage prevalence continues to increase rapidly. Chronic kidney disease is an important

Study of Various Rainfall Estimation Prediction Techniques using Data Mining
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It is important to estimate accurately rainfall for the effective use of water resources and optimal planning of water structures and availability. For this purpose, the various models and techniques are developed to estimate rainfall in various researches using data mining

Enhanced Churn Prediction Model Based on Comparative Analysis of Data Mining Classifier Algorithm
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Subscribe are increasingly terminating their membership agreement with telecommunication companies through mobile number portability (MNP) in order to subscribe to another competitor companies. According to Rijnen (2018), telecommunication companies alone

RESEARCHER REVIEWS ON DATA MINING TECHNIQUES FOR MEDICAL DATASET: A SURVEY
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In advanced computer technologies huge amount of raw data are being generated day by day in every field especially health care industry, to make the raw medical data into useful information to predict and diagnosis the diseases data mining techniques have been used

Introduction to the Minitrack on Data Analytics, Data Mining and Machine Learning for Social Media
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Transforming data from social media into useful information, or knowledge, is the focus of this minitrack. The papers at HICSS in remind our attendees and readers of the many applications of data analytics, data mining and machine learning to social media. At the fiftyIn recent years, the rapid development of e-commerce has brought great convenience to people. Compared with traditional business environment, e-commerce is more dynamic and complex, which brings many challenges. Data mining technology can help people better

INDOOR FIRE DETECTION SYSTEM BASED ON DATA MINING TECHNIQUES
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Fire ignition is one of the events that can cause severe humanitarian, economic, and environmental damages. Fast fire detection can significantly help in controlling the fire and reducing the resulting losses. Efficient fire-fighting in schools can save many lives and

Nature-Inspired Computation in Data Mining and Machine Learning
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Data mining and machine learning are two very active areas of research due to their importance in both theory and practical applications. New methods have been developed and traditional techniques have been enhanced in the last two decades. Accompanying

A review of data mining in knowledge management: applications/findings for transportation of small and medium enterprises
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A core subfield of knowledge management (KM) and data mining (DM) constitutes an integral part of the knowledge discovery in database process. With the explosion of information in the new digital age, research studies in the DM and KM continue to heighten

A Design Theoretic Approach for Smarter Advising using Educational Data Mining
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Student success in higher education remain a topic of interest for academics and educators. While research has been conducted around topics such as attrition, retention, and educational data mining one of the hardest questions to answer is what effect does a

Performance of different data mining methods for predicting rainfall of Rajshahi district, Bangladesh
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To predict rainfall has been treated as one of the most challenging task around the world. Accurate and timely rainfall prediction can be very useful to take necessary safety action for different purposes such as ongoing construction projects, transportation activities

Data Mining in Business Education: Exploratory Analysis of Course Data and Job Market Requirements
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For a number of years data analytics has been showing a tremendous growth and proliferation in multiple fields and organizations. According to the Dresner Advisory Service2018 Big Data Analytics Market Study, a combined big data deployment in If you are a beginner in data mining or you are a visual learner, or you want to understand the mathematical background behind some popular data mining techniques, or you are an educator, or simply because you want to improve your Excel skills, this book is right for you

Soil Fertility Prediction Using Data Mining Techniques
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Agriculture industry in India is the greatest sector for employment but lack of research in this sector is the reason behind less productivity. Its important to implement computational research, Machine learning techniques in Agriculture industry to make India better quality