Use Of Knowledge Discovery Research Articles

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.

Development of J48 Algorithm-Based Application in Predicting Teacher’s Techno-Pedagogical Competence

Education 4.0 in the Philippines encourages the use of advanced technologies in facilitating the educational ecosystem where teachers are required to be competent in information and communications technology (ICT). On the contrary, the established instrument in assessing the pedagogical ICT competence in the Philippines is still a manual process showing a gap between the actual and the ideal in terms of education 4.0 requirement. In this paper, it is empirical to develop a model and implement this model in actual system development where the software learns through time based on the data collected from the system users in predicting the pedagogical ICT competence of the teachers. This study was conducted in a Philippine state university, where the total enumeration of the respondents was taken. The use of Knowledge Discovery in Databases was utilized to develop a model using J48 algorithm, evaluated through 10- cross-fold validation, and used to develop an application that predicts the pedagogical ICT competence of the teacher using R programming. The result showed a significant improvement and efficiency in predicting the pedagogical ICT competence of the teachers.

Sequential pattern mining combined multi-criteria decision-making for farmers’ queries characterization

Agricultural policymakers use various types of expert systems to identify agricultural problems and to explore their potential solutions. However, in the current scenario, there is no robust system that can be used to collect and analyze information regarding the problems faced by farmers of the developing countries on a large scale. This article outlines the possible mechanisms through which information and communication technology (ICT) with the use of Knowledge Discovery in Databases could facilitate agricultural adoption. The goal of this study is to explore data from a farmers’ helpline center as a new medium to gain hidden insights in terms of association rules regarding the problems faced by Indian farmers. The dataset used in this study is collected from the “Kisan Call Center”, a farmers’ helpline center managed by the Ministry of Agriculture, Government of India. For this objective, we propose a new approach that uses association rule mining integrated with a multi-criteria decision-making technique, TOPSIS to extract only the most relevant patterns from the dataset. Later, we perform experiments in order to analyze the output of the proposed framework and verify the discovered knowledge against the validation data. The best experiment generates a rule-set, consisting of 702 association rules, including insights from 25 states of India, with an average confidence value of 73.21% on the validation data. The extracted inference reveals many hidden patterns regarding associations among the farmers’ issues from the remote states of India. Finally, we identify various potential applications of our work and conclude with some possible future developments in the proposed approach.

Drug Prediction System Using Data Mining Techniques - A Survey

The thriving Medical applications of Data mining in the fields of Medicine and Public health has led to the popularity of its use in Knowledge Discovery in Databases (KDD). Data mining has revealed novel Biomedical and Healthcare acquaintances for Clinical decision making that has great potential to improve the treatment quality of hospitals and increase the survival rate of patients. Drug Prediction is one of the applications where data mining tools are establishing the successful results. Data mining intends to endow with a systematic survey of current techniques of Knowledge discovery in Databases using Data mining techniques that are in use in today’s Medical research. To enable the drug retrieval and the breakthrough of hidden retrieval patterns from related databases, a study is made. Also, the use of data mining to discover such relationships as those between Supervised and Unsupervised are presented. This paper summarizes various Machine learning algorithms based on various Data mining techniques in learning strategies. It has also been targeted on contemporary research being done the usage of the Data mining strategies to beautify the retrieval manner. This research paper offers destiny developments of modern-day strategies of KDD, using data mining equipment for medicinal drug industry. It also confers huge troubles and demanding situations related to information mining and medication area. The research discovered a developing quantity of records mining packages, such as evaluation of drugs names for higher fitness policy-making, detection of accurate effects with outbreaks and preventable from misclassified drug names.

Knowledge discovery for enabling smart Internet of Things: A survey

The use of knowledge discovery on the Internet of Things (IoT) and its allied domains is undeniably one of the most indispensable ones, which results in optimized placement architectures, efficient routing protocols, device energy savings, and enhanced security measures for the implementation. The absence of knowledge discovery in IoT results in just an implementation of large‐scale sensor networks, which generates a huge amount of data, and which needs, an often under‐optimized, processing for actionable outputs. In this survey, we explore various domains of IoT for which knowledge discovery is inseparable from the application, and show how it benefits the overall implementation of the IoT architecture.This article is categorized under: Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Machine Learning Application Areas > Internet and Web‐Based Applications Algorithmic Development > Spatial and Temporal Data Mining

Symbolic representation based on trend features for knowledge discovery in long time series

The symbolic representation of time series has attracted much research interest recently. The high dimensionality typical of the data is challenging, especially as the time series becomes longer. The wide distribution of sensors collecting more and more data exacerbates the problem. Representing a time series effectively is an essential task for decision-making activities such as classification, prediction, and knowledge discovery. In this paper, we propose a new symbolic representation method for long time series based on trend features, called trend feature symbolic approximation (TFSA). The method uses a two-step mechanism to segment long time series rapidly. Unlike some previous symbolic methods, it focuses on retaining most of the trend features and patterns of the original series. A time series is represented by trend symbols, which are also suitable for use in knowledge discovery, such as association rules mining. TFSA provides the lower bounding guarantee. Experimental results show that, compared with some previous methods, it not only has better segmentation efficiency and classification accuracy, but also is applicable for use in knowledge discovery from time series.

Future Trends of Data Mining in Predicting the Various Diseases in Medical Healthcare System

The thriving medical applications of data mining in the fields of medicine and public health has led to the popularity of its use in knowledge discovery in databases (KDD). Data mining has revealed novel biomedical and healthcare acquaintances for clinical decision making that has great potential to improve the treatment quality of hospitals and increase the survival rate of patients. Disease diagnosis is one of the applications where data mining tools are establishing the successful results. Data mining intends to endow with a systematic survey of current techniques of knowledge discovery in databases using data mining techniques that are in use in today’s medical research. Discussion is made to enable the disease diagnosis and the breakthrough of hidden healthcare patterns from related databases is offered. Also, the use of data mining to discover such relationships as those between health conditions and a disease is presented. It further discusses about the tools that can be used for the processing and classification of data. This paper summarizes various technical articles on medical diagnosis and prognosis. It has also been focused on current research being carried out using the data mining techniques to enhance the disease(s) forecasting process. This research paper provides future trends of current techniques of KDD, using data mining tools for healthcare. It also confers significant issues and challenges associated with data mining and healthcare in general. The research found a growing number of data mining applications, including analysis of health care centers for better health policy-making, detection of disease outbreaks and preventable hospital deaths. The root causes of all diseases get closer towards drugs i.e. the foremost risk factor of all hilarious diseases. Drug addiction using WEKA has been used that brings into light concerning majority of drug abusers started abusing drugs at age below 20yrs.It is to make aware the druggist about the various diseases that are caused with heavy or long term intake of drugs in their life. So, to make an expert system that will awake the youth about precarious use of drugs and also alert the affected person.

Approaches to Partition Medical Data using Clustering Algorithms

successful application of data mining in fields like e- business, marketing and retail have led to the popularity of its use in knowledge discovery in databases (KDD) in other industries and sectors. Data is a great asset to meet long-term goals of any organization and can help to improve customer relationship management. It can also benefit healthcare providers like hospitals, clinics and physicians, and patients, for example, by identifying effective treatments and best practices popularity of its use in knowledge discovery in databases (KDD) in other industries and sectors. Efficient clustering tools reduce demand on costly healthcare resources. It can help physicians cope with the information overload and can assist in future planning for improved services. Clustering results are used to study independence or correlation between diseases and for better insight into medical survey data. To achieve this, create clustering algorithms that enhances the traditional K-Means, DB-Scan and Fuzzy C-Means algorithms.

Application of data mining techniques in healthcare database

Healthcare system have several and vast databases. Valuable information can be extracted from these data stores through the application of data mining techniques. Data mining is a powerful tool that will help healthcare system to focus on the data warehouse important information as it extracts hidden predictive information from large databases. Classification based data mining techniques such as artificial neural network and rule based for use in knowledge discovery of the massive volume of healthcare data was examined. Neural networks was used because of its ability to adapt and generalize. The rule base depends on a set of expert rules designed to identify different patterns in healthcare data. The rules were created using the knowledge of what characterizes healthcare data. The rules made it possible for the system to automate the processes stabilizing the knowledge gained over time as regards the characteristics of healthcare data.Keywords: Data mining, artificial neural network, rule based, healthcare data, database

Informing Child Welfare Policy and Practice

Proper management and implementation of an effective child welfare agency requires the constant use of information about the experiences and outcomes of children involved in the system, emphasizing the need for comprehensive, timely, and accurate data. In the past 20 years, there have been many advances in technology that can maximize the potential of administrative data to promote better evaluation and management in the field of child welfare. Specifically, this article discusses the use of knowledge discovery and data mining (KDD), which makes it possible to create longitudinal data files from administrative data sources, extract valuable knowledge, and make the information available via a user-friendly public Web site. This article demonstrates a successful project in North Carolina where knowledge discovery and data mining technology was used to develop a comprehensive set of child welfare outcomes available through a public Web site to facilitate information sharing of child welfare data to improve policy and practice.

Implementing EM and Viterbi algorithms for Hidden Markov Model in linear memory

BackgroundThe Baum-Welch learning procedure for Hidden Markov Models (HMMs) provides a powerful tool for tailoring HMM topologies to data for use in knowledge discovery and clustering. A linear memory procedure recently proposed by Miklós, I. and Meyer, I.M. describes a memory sparse version of the Baum-Welch algorithm with modifications to the original probabilistic table topologies to make memory use independent of sequence length (and linearly dependent on state number). The original description of the technique has some errors that we amend. We then compare the corrected implementation on a variety of data sets with conventional and checkpointing implementations.ResultsWe provide a correct recurrence relation for the emission parameter estimate and extend it to parameter estimates of the Normal distribution. To accelerate estimation of the prior state probabilities, and decrease memory use, we reverse the originally proposed forward sweep. We describe different scaling strategies necessary in all real implementations of the algorithm to prevent underflow. In this paper we also describe our approach to a linear memory implementation of the Viterbi decoding algorithm (with linearity in the sequence length, while memory use is approximately independent of state number). We demonstrate the use of the linear memory implementation on an extended Duration Hidden Markov Model (DHMM) and on an HMM with a spike detection topology. Comparing the various implementations of the Baum-Welch procedure we find that the checkpointing algorithm produces the best overall tradeoff between memory use and speed. In cases where sequence length is very large (for Baum-Welch), or state number is very large (for Viterbi), the linear memory methods outlined may offer some utility.ConclusionOur performance-optimized Java implementations of Baum-Welch algorithm are available at . The described method and implementations will aid sequence alignment, gene structure prediction, HMM profile training, nanopore ionic flow blockades analysis and many other domains that require efficient HMM training with EM.

Next generation knowledge management

Despite its explosive growth over the last decade, the Web remains essentially a tool to allow humans to access information. The next generation of the Web, dubbed the ‘Semantic Web’, will extend the Web’s capability through the increased availability machine-processable information. These machine-processable descriptions of Web information resources are called meta-data and are associated with ontologies, or conceptualisations of the domain of application. Meta-data and associated ontologies then allows more intelligent software systems to be written, automating the analysis and exploitation of Web-based information. This paper describes how knowledge management can be improved through the adoption of Semantic Web technology. To realise this, a number of different technologies need to be brought together. Their fusion provides the infrastructure which makes semantic knowledge management possible. Specifically, the paper discusses the use of knowledge discovery and human language technology to (semi-)automatically derive the required ontologies and meta-data, along with a methodology to support this process. We describe techniques for management and controlled evolution of ontologies and a set of semantic knowledge access tools for enhanced information access. Finally, a set of application scenarios for the technology are sketched.

Computing iceberg concept lattices with T itanic

We introduce the notion of iceberg concept lattices and show their use in knowledge discovery in databases. Iceberg lattices are a conceptual clustering method, which is well suited for analyzing very large databases. They also serve as a condensed representation of frequent itemsets, as starting point for computing bases of association rules, and as a visualization method for association rules. Iceberg concept lattices are based on the theory of Formal Concept Analysis, a mathematical theory with applications in data analysis, information retrieval, and knowledge discovery. We present a new algorithm called T itanic for computing (iceberg) concept lattices. It is based on data mining techniques with a level-wise approach. In fact, T itanic can be used for a more general problem: Computing arbitrary closure systems when the closure operator comes along with a so-called weight function. The use of weight functions for computing closure systems has not been discussed in the literature up to now. Applications providing such a weight function include association rule mining, functional dependencies in databases, conceptual clustering, and ontology engineering. The algorithm is experimentally evaluated and compared with Ganter's Next-Closure algorithm. The evaluation shows an important gain in efficiency, especially for weakly correlated data.

Knowledge discovery from databases: an introductory review

The paper aims to provide a non‐technical introduction to the new procedures being used to extract knowledge from databases. The reasons for developing knowledge discovery methods are discussed ± primarily, the current production of very large databases that may include many data relations not explicit in the database structure. The background in machine learning is indicated. The methods used are described for such techniques as classification (sorting data into predefined classes), clustering (developing ab initio a data classification) and the detection of deviations from pre‐established norms. Examples of the applications of these methods are given. The paper concludes with some brief thoughts about the potential use of knowledge discovery in the information field.