Development Of Data Mining Techniques Research Articles

Analyzing categorical time series with the R package ctsfeatures

Time series data are ubiquitous nowadays. Whereas most of the literature on the topic deals with real-valued time series, categorical time series have received much less attention. However, the development of data mining techniques for this kind of data has substantially increased in recent years. The R package ctsfeatures offers users a set of useful tools for analyzing categorical time series. In particular, several functions allowing the extraction of well-known statistical features and the construction of illustrative graphs describing underlying temporal patterns are provided in the package. The output of some functions can be employed to perform traditional machine learning tasks including clustering, classification and outlier detection. The package also includes two datasets of biological sequences introduced in the literature for clustering purposes, one dataset of sleep stages, and three interesting synthetic databases. In this work, the main characteristics of the package are described and its use is illustrated through various examples. Practitioners from a wide variety of fields could benefit from the valuable tools provided by ctsfeatures.

Diagnosis of Thyroid Disease: Comparison of Adaptive Neural Fuzzy Inference System and Artificial Neural Network with the Logistic Regression Model

Background: The development of data mining techniques and the adaptive neuro-fuzzy inference system (ANFIS) in the last few decades has made it possible to achieve accurate predictions in medical fields. Objectives: The present study aimed to use the ANFIS model, artificial neural network (ANN), and logistic regression to predict thyroid patients. Methods: This study aimed to predict thyroid disease using the UCI database, ANFIS and ANN models, and logistic regression. We only used four of its features as the input of the model and considered thyroid as a binary response (occurrence=1, non-occurrence=0) as the output of the model. Finally, three models were compared based on the accuracy and the area under the curve (AUC). Results: In this study, out of the extensive UCI database, which includes 3,772 samples and over 20 features, only five specific features were utilized. Data include 1,144 males and 2,485 females. The results of multiple logistic regression analysis demonstrated that free T4 index (FTI) and thyroid stimulating hormone (TSH) had a significant effect on thyroid. The ANFIS model had a higher accuracy (99%) compared to ANN (96%) and the logistic regression model (94%) in the prediction of thyroid. Conclusion: As evidenced by the obtained results, the forecasting performance of ANFIS is more efficient than other models. Moreover, the use of combined methods, such as ANFIS, to diagnose and predict diseases increases the accuracy of the model. Therefore, the results of this study can be used for screening programs to identify people at risk of thyroid disease.

Extracting product design guidance from online reviews: An explainable neural network-based approach

With the development of data mining techniques, user-generated data has become a valuable resource in diverse research areas. In product design research, many studies have been utilizing user data to discover implications for new product design. However, previous works focused on analyzing existing features, whereas companies also need strategies for new features. Some studies discovered new feature ideas from user data but did not provide design implications. This paper addresses the above limitation by extracting comprehensive design implications for both features from user data. The method first defines the lists of existing/new features and collects spec data for these features. Then, it constructs customer choice sets based on the online review and spec data. Regarding spec values, this study presents a newness merit function that reflects the changing value of new features and applies it to the choice sets. The final stage trains a neural network model based on choice sets and conducts SHAP (SHapley Additive exPlanations) on the model. The method draws design implications by further analyzing the resultant SHAP values. The suggested methodology was tested on real-world datasets. The result provides design guidance, including strategies for new features and recommended spec ranges for existing features. This article validates the result by showing that the obtained design implications are consistent with previous market research for product features.

Skin Disease Prediction

Machine Learning (ML) is an emerging technology and a discipline to study how to use the machine to simulate human learning activities. It focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. Prediction in ML involves using a trained model to make predictions on forecasts, about new unseen data based on patterns learned from past data. On the contrary healthcare plays an important role in human life. Healthcare presents a range of complex and challenging problems that require sophisticated solutions. For example, accurately diagnosing diseases based on medical images can be difficult, but ML models can be trained to identify subtle patterns that might be missed by human experts. Skin diseases are very common nowadays and spreading widely among people in present time. Therefore, development of data mining techniques can efficiently distinguish classes of skin disease is important. Due to lack of medical facilities available in the remote areas, patients usually ignore early symptoms which may worsen the situation as time progresses. Hence, there is a rising need for automatic skin disease detection system with high accuracy. Therefore, an application is developed which uses Convolutional Neural Network (CNN) as it is the most efficient algorithm for image classification. Then a machine learning model is built for better accuracy. Then Softmax is used for multi classification. Deep Learning is used to train the model, Deep Learning is a part of Machine Learning in which unlike Machine Learning it uses large dataset and hence the number of classifiers is reduced substantially. The final model will be able to identify and classify the type of disease which has affected a particular area of skin.

Classification of Privacy Preserving Data Mining Algorithms: A Review

Nowadays, data from various sources are gathered and stored in databases. The collection of the data does not give a significant impact unless the database owner conducts certain data analysis such as using data mining techniques to the databases. Presently, the development of data mining techniques and algorithms provides significant benefits for the information extraction process in terms of the quality, accuracy, and precision results. Realizing the fact that performing data mining tasks using some available data mining algorithms may disclose sensitive information of data subject in the databases, an action to protect privacy should be taken into account by the data owner. Therefore, privacy preserving data mining (PPDM) is becoming an emerging field of study in the data mining research group. The main purpose of PPDM is to investigate the side effects of data mining methods that originate from the penetration into the privacy of individuals and organizations. In addition, it guarantees that the data miners cannot reveal any personal sensitive information contained in a database, while at the same time data utility of a sanitized database does not significantly differ from that of the original one. In this paper, we present a wide view of current PPDM techniques by classifying them based on their taxonomy techniques to differentiate the characteristics of each approach. The review of the PPDM methods is described comprehensively to provide a profound understanding of the methods along with advantages, challenges, and future development for researchers and practitioners.

Robustness of Adaptive Neuro- Fuzzy Inference System for Optimal Prediction using Roulette Wheel Method

An information system that supports automatic decision making with help of intelligent system by computerized manner. The proposed work has been developed and deployed a robust method is contributed to decision making in medical system and the diagnosis the major risk of the patients in earlier. The main goal of the proposed research is to develop data mining techniques to support decision making and to control the controllable risk factors and also overcome the other parts of organs highly affected by diabetes, kidney disease, heart condition and which in turn reduces the risk of the patients. Robustness of Adaptive Neuro-Fuzzy Inference System (RANFIS) designed a fuzzy inference system (FIS) to enrich the knowledge about the data set whose membership function parameters can be altered randomly by the process of mutation.

Variance Risk Identification and Treatment of Clinical Pathway by Integrated Bayesian Network and Association Rules Mining

With the continuous development of data mining techniques in the medical field, variance analysis in clinical pathways based on data mining approaches have attracted increasing attention from scholars and decision makers. However, studies on variance analysis and treatment of specific kinds of disease are still relatively scarce. In order to reduce the hazard of postpartum hemorrhage after cesarean section, we conducted a detailed analysis on the relevant risk factors and treatment mechanisms, adopting the integrated Bayesian network and association rule mining approaches. By proposing a Bayesian network model based on regression analysis, we calculated the probability of risk factors determining the key factors that result in postpartum hemorrhage after cesarean section. In addition, we mined a few association rules regarding the treatment of postpartum hemorrhage on the basis of different clinical features. We divided the risk factors into primary and secondary risk factors by realizing the classification of different causes of postpartum hemorrhage after cesarean section and sorted the posterior probability to obtain the key factors in the primary and secondary risk factors: uterine atony and prolonged labor. The rules of clinical features associated with the management of postpartum hemorrhage during cesarean section were obtained. Finally, related strategies were proposed for improving medical service quality and enhancing the rescue efficiency of clinical pathways in China.

Game Data Mining Competition on Churn Prediction and Survival Analysis Using Commercial Game Log Data

Game companies avoid sharing their game data with external researchers. Only a few research groups have been granted limited access to game data so far. The reluctance of these companies to make data publicly available limits the wide use and development of data mining techniques and artificial intelligence research specific to the game industry. In this work, we developed and implemented an international competition on game data mining using commercial game log data from one of the major game companies in South Korea: NCSOFT. Our approach enabled researchers to develop and apply state-of-the-art data mining techniques to game log data by making the data open. For the competition, data were collected from Blade & Soul, an action role-playing game, from NCSOFT. The data comprised approximately 100 GB of game logs from 10,000 players. The main aim of the competition was to predict whether a player would churn and when the player would churn during two periods between which the business model was changed to a free-to-play model from a monthly subscription. The results of the competition revealed that highly ranked competitors used deep learning, tree boosting, and linear regression.

STCLARanS: An Improved Clustering Large Applications based on Randomized Search Algorithm using Slim-tree Technique

Clustering has been used for data interpretation when dealing with large database in the fields of medicines, business, engineering etc. for the recent years. Its existence paved way on the development of data mining techniques like CLARANS (Clustering Large Applications based on Randomized Search) Algorithm. It is the most efficient k-medoids technique that uses randomized strategy to identify the best medoids in a large dataset. Likewise, it surpasses the clustering performance of both PAM (Partitioning Around Medoids) and CLARA (Clustering Large Applications) in terms of time. This paper addresses the task of integrating Slim-tree method to CLARANS for the development of the proposed Slim-tree Clustering Large Applications based on Randomized Search (STCLARanS) Algorithm and an experimental evaluation was prepared using synthetic and real datasets for the comparison of the quality of the clustered output of the CLARANS and the proposed STCLARanS algorithms. The Slim-tree method is used for pre-clustering of the objects in the dataset in identifying the objects in the middle level as the sample objects used to start the clustering process. The proposed Algorithm assumes that with the new sampling strategy to draw the initial cluster centers to start the clustering process may yield to better quality of the clustered outputs as compared to the clustered output of the CLARANS algorithm. The quality of the clustered output is measured on the accumulated distances of the objects to their cluster centers.

A Taxonomy of Data Mining Problems

Much of the research in data mining and knowledge discovery has focused on the development of efficient data mining algorithms. Researchers and practitioners have developed data mining techniques to solve diverse real-world data mining problems. But there is no single source that identifies which techniques solve what problems and how, the advantages and limitations, and real-life use-cases. Lately, identifying data mining techniques and corresponding problems that they solve has drawn significant attention. In this paper, the author describes the progress made in developing data mining techniques and then classify them in terms of data mining problems taxonomy to help assist practitioners in using appropriate data mining techniques that solve business problems. This will allow researchers to expand the body of knowledge in this discipline. This article proposes a data mining problems taxonomy based on data mining techniques being used. Prominent data mining problems include classification, optimization, prediction, partitioning, relationship, pattern matching, recommendation, ranking, sequential patterns and anomaly detection. The data mining techniques that are used to solve these data mining problems in general fall under top 10 data mining algorithms.

An ontology-based framework for the management of machining information in a data mining perspective

The advent and fast development of data mining techniques induced, in every field, interest for the data produced and stored. Machining process planning is no exception to this rule, and, with the important amount of related data that is stored in the enterprise information system, the application of data mining seems promising. However, the strong heterogeneity in data, and the distribution of information throughout several different documents, may hinder the application of data-mining methods and machine learning tools.This paper will introduce a knowledge-based engineering framework which can be used as an information system able to query consistent, correct and complete data from the heterogeneous corpus of documents used in process planning of machined parts.

Identification of Protein Biomarkers for Diabetic Retinopathy using Sequence Mining Techniques

Bioinformatics and sequence mining are the application and development of data mining techniques to solve problems by comprehending biological data. Sequence analysis is the most primitive operation in sequence mining techniques. Modern sequence mining research is specialized in analyzing sequential patterns which are relevant and distinct from one another and utilizing retrieved sequences similarity and distance between different protein sequences can be analyzed. Diabetic retinopathy is the major cause of blindness in individuals mostly adults with diabetes and is it is the common problem of diabetes mellitus across the world. Various research analyses stated that there are many proteins which are found to take part in diabetic retinopathy. In this paper, we have evaluated certain proteins which are closely related with diabetic retinopathy with the help of multiple alignment tool viz. Clustal Omega and obtained a phylogenetic tree of 28 protein sequences gathered from National Center for Biotechnology Information (NCBI). In this work data mining technique called sequence mining plays a significant role in providing phylogram obtained with Neighbor-Joining algorithm. From the phylogenetic tree it was recognized that cortistatin, vitamin-D receptor and somatostatin proteins has close connection with diabetic retinopathy. Molecular docking studies have also been performed which is the most extensively used method for the calculation of protein-ligand interactions. In silico docking studies indicated that four inhibitory compounds i.e. Quercetin, Kaempferol, Naringenin and Melicitrin interact with aldose reductase which also found to have role in diabetic retinopathy. Outcomes infer that techniques intended to standardize cortistatin, vitamin-D receptor and somatostatin activities be of huge advantage and provide benefit in inhibiting diabetic retinopathy.

Digital Greek Patristic Catena (DGPC). A brief presentation

The project is to develop a database, which is planned to include all available information on the use of the Bible in the patristic works of Migne's Patrologia Graeca. Utilization of the data will be available through a web page equipped with necessary tools for developing data mining techniques and other methods of analysis. The main aim of the project is to revive the catenae, the ancient exegetical tool for biblical interpretation.

Data and knowledge mining with big data towards smart production

Abstract Driven by the innovative improvement of information and communication technologies (ICTs) and their applications into manufacturing industry, the big data era in manufacturing is correspondingly arising, and the developing data mining techniques (DMTs) pave the way for pursuing the aims of smart production with the real-time, dynamic, self-adaptive and precise control. However, lots of factors in the ever-changing environment of manufacturing industry, such as, various of complex production processes, larger scale and uncertainties, more complicated constrains, coupling of operational performance, and so on, make production management face with more and more big challenges. The dynamic inflow of a large number of raw data which is collected from the physical manufacturing sites or generated in various related information systems, caused the heavy information overload problems. Indeed, most of traditional DMTs are not yet sufficient to process such big data for smart production management. Therefore, this paper reviews the development of DMTs in the big data era, and makes discussion on the applications of DMTs in production management, by selecting and analyzing the relevant papers since 2010. In the meantime, we point out limitations and put forward some suggestions about the smartness and further applications of DMTs used in production management.

Survey on Privacy Preserving Association Rule Data Mining

The progress in the development of data mining techniques achieved in the recent years is gigantic. The collative data mining techniques makes the privacy preserving an important issue. The ultimate aim of the privacy preserving data mining is to extract relevant information from large amount of data base while protecting the sensitive information. The togetherness in the information retrieval with privacy and data quality is crucial. A detailed survey of the present methodologies for the association rule data mining and a review of the state of art method for privacy preserving association rule mining is presented in this paper. An analysis is provided based on the association rule mining algorithm techniques, objective measures, performance metrics and results achieved. The metrics and the short comings of the various existing technologies are also analysed. Finally, the authors present various research issues which can be useful for the researchers to accomplish further research on the privacy preserving association rule data mining.

A Multi-Class Based Algorithm for Finding Relevant Usage Patterns from Infrequent Patterns of Large Complex Data

Background/Objective: With the development of data mining techniques to analyze large amount of complex data has played an essential role in several areas like cloud computing, medical databases, geographical information retrieval, etc. The automatic evaluation of cloud patterns is a challenging task due to the large amount of interesting patterns can be extracted. However, how to find infrequent patterns is still an open issue in cloud computing. Methods: Conventional approaches are mainly depends on quantitative datasets with support and confidence measures. Due to the large amount of cloud storage data, it is very difficult to extract the weighted association rules based on the server usage statistics. Traditional techniques are implemented on the data samples with the same attribute type. Due to this fact, a multi-class algorithm is proposed to find relevant usage patterns of large complex data. Findings: Proposed approach does not rely on any probabilistic closure measures and quantitative data. This approach minimizes the database scans and optimizes the infrequent cloud patterns. Applications/Improvements: Experimental results show that, proposed work generates high quality cloud patterns compared to traditional quantitative rule mining techniques.

A Survey of Data Mining Methods for Automated Diagnosis of Cardiac Autonomic Neuropathy Progression

Cardiac autonomic neuropathy (CAN) is a disease that occurs as a result of nerve damage causing an abnormal control of heart rate. CAN is often associated with diabetes and is important, as it can lead to an increased morbidity and mortality of the patients. The detection and management of CAN is important since early intervention can prevent further complications that may lead to sudden death from myocardial infarction or rhythm disturbance. This paper is devoted to a review of work on developing data mining techniques for automated detection of CAN. A number of different categorizations of the CAN progression have been considered in the literature, which could make it more difficult to compare the results obtained in various papers. This is the first review proposing a comprehensive survey of all categorizations of the CAN progression considered in the literature, and grouping the results obtained according to the categorization being dealt with. This novel, thorough and systematic overview of all categorizations of CAN progression will facilitate comparison of previous results and will help to guide future work.

Data Mining and its Application in Modern Animal Husbandry

Modern animal husbandry information technology, networking and intelligent development of data mining techniques proposed requirements. This paper analyzes the data mining for the importance of modern animal husbandry management, data warehouse, data mining technology, focusing on the analysis of data mining in the mining process in the modern animal husbandry management decisions and implementation of technology, pointed out the data mining technology provide more powerful support functions for modern animal husbandry management decisions.

Editorial: Issues in quantitative healthcare research

Editorial: Issues in quantitative healthcare research

Bit Transformation Perturbative Masking Technique for Protecting Sensitive Information In Privacy Preserving Data Mining

The goal of data mining is ascertaining novel and valuable knowledge from data. In many situations, the extracted knowledge is highly confidential and it needs sanitization before giving to data mining researchers and the public in order to address privacy concerns. There have been two types of privacy in data mining. The first type of privacy is that the data is altered so that the mining result will preserve certain privacy. The second type of privacy is that the data is manipulated so that the mining result is not affected or minimally affected. The aim of privacy preserving data mining researchers is to develop data mining techniques that could be applied on data bases without violating the privacy of individuals. Many techniques for privacy preserving data mining have come up over the last decade. Some of them are statistical, cryptographic, randomization methods, k-anonymity model, l-diversity and etc. In this work, we propose a new perturbative masking technique called bit transformation technique for protecting the sensitive information. An experimental result shows that the proposed technique gives the better result compared with the existing micro-aggregation technique.