Data Mining Classification Algorithms for Hepatitis and Thyroid Data Set Analysis

In knowledge management process, data mining technique can be used to extract and discover the valuable and meaningful knowledge from a large amount of data. Nowadays, data mining has given a great deal of concern and attention in the information industry and in society as a whole. This technique is an approach that is currently receiving great attention in data analysis and it has been recognized as a newly emerging analysis tool (Osei-Bryson, 2010; Park, 2001; Sinha, 2008; Tso & Yau, 2007; Wan, 2009; Zanakis, 2005; Zhuang et al., 2009). Additionally, among the major tasks in data mining are classification and prediction; concept description; rule association; cluster analysis; outlier analysis; trend and evaluation analysis; statistical analysis and others. Classification and prediction tasks are among the popular tasks in data mining; and widely used in many areas especially for trend analysis and future planning. In fact, classification technique is supervised learning, which is the class level or prediction target is already known. As a result, the classification model which is represented through rules structures will be constructed in the classification process. In this case, the constructed model will be representing the precious knowledge and it can be used for future planning. There are many areas which adapted this approach to solve their problems such as in finance, medical, marketing, stock, telecommunication, manufacturing, health care, customer relationship and etc. However, the data mining application has not attracted much attention from people in Human Resource (HR) field (Chien & Chen, 2008; Ranjan, 2008). Besides that, in our previous study, most of the prediction applications are used to predict stock, demand, rate, risk, event and others; but there are quite limited studies on human prediction. In addition prediction applications are mainly developed in business and industrious fields; and quite restricted studies involved human talent in an organization (Jantan et al., 2009). HR data can provide a rich resource for knowledge discovery and for decision support system development. Recently, an organization has to struggle effectively in term of cost, quality, service or innovation. All these depend on having enough right people with the right skills, employed

Pattern mining algorithms for data streams using itemset

Data mining is defined as the upcoming field that consists of certain tools and techniques to be implemented with certain data sets taken from the different sources to foresee the hidden information. The data mining is the huge upcoming field has attracted many fields under its influence. In the applications of data mining, health care is a very important application to be taken account. Healthcare is defined as the service provides the health maintenance and earlier disease prediction and also provides high quality treatments to prevent disease. Human body consists of a number of cells constituted to form organs and the organs connected to form the organ system. This system should be interconnected to work properly. The human body should be nourished properly by balanced diet and the healthy lifestyle. The function of the human body is disturbed by some external factors called disease. The metabolic disease is the collection of five different disorders such as high blood pressure, heart problems, obesity and insulin resistance. The Optical Coherence Tomography images of eyes are considered to predict the chronic conditions of the body accurately in the eyes. The main focus of this work is to detect diabetes through the retina images. This paper mainly reflects detection of diabetes using retina images. In this paper the classification techniques are analyzed using orange data mining tool to find the best classification technique based on the individual technique’s prediction accuracy.

Data mining aims to develop a new generation of tools to intelligently assist humans in analyzing mountains of data. Over the past few years, great progress has been made in both research and applications of data mining. Data mining systems have helped many businesses by exposing previously unknown patterns in their databases, which were used to improve profits, enhance customer services, and ultimately achieve a competitive advantage. In this paper, we present our unique data mining system DM-II (Data Mining Integration and Interestingness). DM-II is a PC-based system working in Windows 95/98/NT environment. Apart from the normal components of a data mining system, DM-II has a number of unique and advanced sub-systems. These sub-systems have been applied in many real-life applications, including education applications, insurance application, accident application, disease application, drug screening application, image classification, etc. Here, we focus on discussing the following sub-systems: CBA: CBA originally stands for Classification-Based on Associations [lo]. It has now been extended with a number of other advanced features. In all, CBA has the following capabilities: Building classifiers using association rules: Traditionally, association rule mining and classifier building are regarded as two different data mining tasks. CBA unifies the two tasks. It is able to ,generate association rules and builds a classifier using a special subset of the association rules. What is significant is that CBA, in general, produces more accurate classifiers compared to the state-ofthe-art classification system C4.5 [ 161. It also helps to solve some outstanding problems with the existing classification systems. Pruning and summarizing the discovered associations: One major problem with association

Java has become a language of choice for applications executing in heterogeneous environments utilising distributed objects and multithreading. To handle large data sets, scalable and efficient implementations of data mining approaches are required, generally employing computationally intensive algorithms. Conventional Java implementations do not directly provide support for the data structures often encountered in such algorithms, and they also lack repeatability in numerical precision across platforms. This paper describes a distributed framework employing task and data parallelism, and implemented in high performance Java (HPJava). Issues of interest for data mining algorithms are identified, and possible solutions discussed for overcoming limitations in the Java Virtual Machine. The framework supports parallelism across workstation clusters, using the message-passing interface as middleware, and can support different analysis algorithms, wrapped as Java objects, and linked to various databases using the Java database connectivity interface. Guidelines are provided for implementing parallel and distributed data mining on large data sets, and a proof-of-concept data mining application is analysed using a neural network.

A cosmetic cheerful shop located in the Marelan area of Medan city is a shop that sells women's cosmetic equipment. The owner of a cheerful cosmetics shop is very disciplined in recording sales and purchases of goods. Every transaction is always recorded and stored in the Microsoft Excel application. Meanwhile, the Ceria Cosmetics store has been running since 2015 and has recorded lots of transactions, both sales and purchases. From 2015 until now the data has been increasing, and so far this data has only been a record and stored in a folder on the store's computer. From the piles of transaction data at the cheerful cosmetics store, the authors took the idea of research to utilize this pile of data. One of the applications of information technology that can be used to utilize the pile of data is the data mining technique. By using data mining, a pile of data from a cheerful cosmetic store can be used to find association patterns. And the application of data mining with association rules can use the FP-Growth algorithm. The FP-Growth algorithm can be used as an alternative to determine the most frequently occurring data set (frequent itemset) in a data set. Later, the combination patterns obtained in this study can be used to arrange the layout of goods (cosmetics) at the Ceria Cosmetics store in Medan Marelan.

Classification is a machine learning procedure that tags data instances into predefined class labels which are used to predict the data according to those Classes. Many classification algorithms in data mining have been stated, such as: C4.5, Apriori, Genetic algorithm, and Fuzzy set approaches which mainly uses heuristic or greedy search to get frequent sets in data for classification, resulting in high error ratio. Recently, a new method for classification has been proposed, called the Classification Using Association also known as associative classification. The main purpose for this is to mine class-association rules. Associative Classification has more advantages than the heuristic and greedy method, as it easily removes noise and higher accuracy is obtained. It additionally generates a rule set, that are more complete than traditional classification methods. This paper presents a meticulous survey on various Associative classification techniques. Moreover, a comparative analysis of accuracy and efficiency of those methods is presented.

A significant amount of attention appears to be focusing on how to better collect, analyze, and disseminate information. In doing so, technology is commonly and increasingly looked upon as both a tool, and, in some cases, a substitute, for human resources. One such technology that is playing a prominent role in homeland security initiatives is data mining. Similar to the concept of homeland security, while data mining is widely mentioned in a growing number of bills, laws, reports, and other policy documents, an agreed upon definition or conceptualization of data mining appears to be generally lacking within the policy community (Relyea, 2002). While data mining initiatives are usually purported to provide insightful, carefully constructed analysis, at various times data mining itself is alternatively described as a technology, a process, and/or a productivity tool. In other words, data mining, or factual data analysis, or predictive analytics, as it also is sometimes referred to, means different things to different people. Regardless of which definition one prefers, a common theme is the ability to collect and combine, virtually if not physically, multiple data sources, for the purposes of analyzing the actions of individuals. In other words, there is an implicit belief in the power of information, suggesting a continuing trend in the growth of “dataveillance,” or the monitoring and collection of the data trails left by a person’s activities (Clarke, 1988). More importantly, it is clear that there are high expectations for data mining, or factual data analysis, being an effective tool. Data mining is not a new technology but its use is growing significantly in both the private and public sectors. Industries such as banking, insurance, medicine, and retailing commonly use data mining to reduce costs, enhance research, and increase sales. In the public sector, data mining applications initially were used as a means to detect fraud and waste, but have grown to also be used for purposes such as measuring and improving program performance. While not completely without controversy, these types of data mining applications have gained greater acceptance. However, some national defense/homeland security data mining applications represent a significant expansion in the quantity and scope of data to be analyzed. Moreover, due to their security-related nature, the details of these initiatives (e.g., data sources, analytical techniques, access and retention practices, etc.) are usually less transparent.

Data Mining is a technique for finding, searching, or extracting new information or knowledge from a very large set of data, by integration or merging with other disciplines such as statistics, artificial intelligence, and machine learning, making Data Mining as one of the tools to analyze data and then produce useful information. Association Rule is a process in Data Mining to determine all associative rules that meet the minimum requirements for support (minsup) and confidence (minconf) in a database. In Association Rule, there are 2 methods that can be used, namely a priori method and FP-Growth method, where FP-Growth method is the development of a priori method where a priori method there are still some shortcomings such as there are many patterns of data combinations that often appear (many frequent patterns), many types of items but low minimum support fulfillment, it takes quite a long time because database scanning is done repeatedly to get the ideal frequent pattern. In this study the method used is a priori algorithm method, a priori algorithm method is one of the alternative ways to find the most frequently appearing data sets (frequent itemset) without using candidate generation that is suitable for analyzing a transaction data. Coffee analysis is a Cafe Shop engaged in the sale of food and beverages that many food and beverage sales transactions. Open on November 7, 2021 coffee analysis penetrates 245 sales transactions and this transaction data continues to grow every day.

Objective Training monitoring is an important part of scientific training, and also accumulated a large amount of data, but the analysis and evaluation of biochemical indicators are mostly concentrated on the level of experience and the general, phased and individualized research application of statistical methods. The data mining technology is applied to the analysis and evaluation of the biochemical indexes of competitive sports, the analysis of the data is carried out in the deep level, the potential, new and useful information and knowledge are extracted, and the new exploration ideas are carried out for the analysis of the biochemical indexes of competitive sports, and a more reliable and more powerful data branch is provided for the scientific and efficient training support.
 Methods Using the literature data method, logic analysis method and expert interview method, the application of the current data mining technology in the analysis of biochemical indicators is summarized.
 Results The scientific analysis and evaluation of athletes' physical function status has been the focus of domestic and foreign coaches and sports researchers. The application of data mining technology in sports biochemical indicators is also becoming more and more extensive. For example, Mao Jie and others applied the gray ART clustering model analysis method to the monitoring of competitive sports biochemical indicators. Through this data mining model, the coach can easily judge the athlete's competitive physical condition, and can provide a scientific basis for correct training according to the different competitive conditions of each athlete, using different training guidance programs and training methods. Ma Jing et al. explored the feasibility of applying decision tree algorithm and association rules in volleyball biochemical analysis. It was found that C5.0 decision tree and Apriori association rule algorithm can be used to predict and analyze the technical level of women's volleyball players. Li Guangjun and others successfully applied the association rule data mining to the biochemical data analysis of canoeists, and provided a basis for scientific decision-making and analysis of sports training and athlete selection. Zhang Hui designed a data mining system for sports biochemical index based on association rules. The results show that the system has fast data mining rate, short time consuming and high reliability. It provides a more scientific evaluation standard for the data mining of sports biochemical index, and also provides a basis for the future training program.
 Conclusions With the development of competitive sports, in order to achieve new heights, the application of data mining technology to vast biochemical data is of great significance for the establishment of scientific training evaluation methods and standards, and is also the inevitable development of future sports scientific research.

On using prototype reduction schemes to optimize kernel-based nonlinear subspace methods

Data mining provides the methodology and technology to transform huge amount of data into useful information for decision making. It is a powerful process to extract knowledge and discover new patterns embedded in large data sets. Data mining has been increasingly used in medicine, particularly in cardiology. In fact, data mining applications can greatly benefits all parts involved in cardiology such as patients, cardiologists and nurses. This article aims to perform a systematic mapping study so as to analyze and synthesize empirical studies on the application of data mining techniques in cardiology. A total of 142 articles published between 2000 and 2015 were therefore selected, studied and analyzed according to the four following criteria: year and channel of publication, research type, medical task and empirical type. The results of this mapping study are discussed and a list of recommendations for researchers and cardiologists is provided.

Inter-transactional association rules for multi-dimensional contexts for prediction and their application to studying meteorological data

Background/Objectives: Organizing large amount of data is the biggest challenge in domains of data mining. One way to deal with these kind of issues is by grouping data taking into account the matter of group of files. Clustering is a widely used technique in data mining application for discovering patterns in original data. Many of the algorithms almost all customized clustering algorithms are restricted in taking care and managing datasets that consists of unconditional characteristics and attributes. However, datasets with unconditional types of attributes are common in real life data mining problem. Clustering is a fundamental data analysis method. It is a way to form natural groupings in the given set of data. K-means is a simple unsupervised clustering techniques. Neural network clustering tool is best for obtaining optimal clustering of large data set as it uses unsupervised competitive technique and clusters by liner dicrimant. It is a form of unsupervised learning where generally we do not have examples demonstrating how the data should be grouped together [7]. Methodology: Existing iris flower dataset is preloaded in MATLAB and is used for clustering into three different species. The dataset is clustered using the k-means algorithm and neural network clustering tool in MATLAB. Neural network clustering tool is mainly used for clustering large data set without any supervision. It is also used for pattern recognition, feature extraction, vector quantization, image segmentation, function approximation, and data mining. Results/Findings: The results include the clustered iris dataset into three species without any supervision. The neighboring cluster's/neuron's distances and weights have also been plotted using self organizing map for better understanding of unsupervised clustering. As an unsupervised classification technique, clustering - 2 - identifies some inherent structures present in a set of objects based on a similarity measure. Clustering techniques are formed on the basis of statistical model identification or competitive learning. The research paper shows detailed outline of competitive learning formed on clustering methods like k-means and obtaining optimal clustering of iris dataset using neural network clustering tool.

Data mining has become a major academic research area over the last ten years. However, in the transition from academic prototypes to commercial products there have been few successes with commercial data mining applications failing to make any significant impact in the marketplace. In this paper it is argued that most data mining applications concentrate on the model-building phase of the data mining process and rarely engage the user in other stages. The paper reviews the challenges of producing successful data mining applications and in particular the role of information visualisation. Visualisation in data mining tends to be used to present final results, rather than playing an important part throughout the entire process. We argue that an immersive environment may provide the user with a more suitable interface than is commonly offered. We present a virtual data mining environment which attempts to integrate a data mining application interface and information visualisation in a seamless manner, using the concept of 'liquid data'.