Application Of Data Mining Methods Research Articles

Predicting the Pro-Longevity or Anti-Longevity Effect of Model Organism Genes with New Hierarchical Feature Selection Methods.

Ageing is a highly complex biological process that is still poorly understood. With the growing amount of ageing-related data available on the web, in particular concerning the genetics of ageing, it is timely to apply data mining methods to that data, in order to try to discover novel patterns that may assist ageing research. In this work, we introduce new hierarchical feature selection methods for the classification task of data mining and apply them to ageing-related data from four model organisms: Caenorhabditis elegans (worm), Saccharomyces cerevisiae (yeast), Drosophila melanogaster (fly), and Mus musculus (mouse). The main novel aspect of the proposed feature selection methods is that they exploit hierarchical relationships in the set of features (Gene Ontology terms) in order to improve the predictive accuracy of the Naïve Bayes and 1-Nearest Neighbour (1-NN) classifiers, which are used to classify model organisms' genes into pro-longevity or anti-longevity genes. The results show that our hierarchical feature selection methods, when used together with Naïve Bayes and 1-NN classifiers, obtain higher predictive accuracy than the standard (without feature selection) Naïve Bayes and 1-NN classifiers, respectively. We also discuss the biological relevance of a number of Gene Ontology terms very frequently selected by our algorithms in our datasets.

The Evaluation on Data Mining Methods of Horizontal Bar Training Based on BP Neural Network

With the rapid development of science and technology, data analysis has become an indispensable part of people’s work and life. Horizontal bar training has multiple categories. It is an emphasis for the re-search of related workers that categories of the training and match should be reduced. The application of data mining methods is discussed based on the problem of reducing categories of horizontal bar training. The BP neural network is applied to the cluster analysis and the principal component analysis, which are used to evaluate horizontal bar training. Two kinds of data mining methods are analyzed from two aspects, namely the operational convenience of data mining and the rationality of results. It turns out that the principal component analysis is more suitable for data processing of horizontal bar training.

Spatio-temporal analysis of Quaternary deposit landslides in the Three Gorges

Quaternary deposit landslides are a representative type of geological disasters in the Three Gorges area. Landslide medium–long-term forecast results offer an important and effective measure to prevent landslide disasters and help guarantee operation security of the reservoir. We applied data mining methods into landslide spatio-temporal data analysis to discover the cause mechanisms, triggered factors and deformation laws, as well as provided medium–long-term forecast criteria for Quaternary deposit landslides in Zigui County in the Three Gorges. We showed that data mining methods used in this study offer an interesting and useful vehicle supporting analysis of a huge amount of landslide spatio-temporal data. The established forecast model exhibits high accuracy, while the discovered forecast criteria come with high confidence and representation capabilities.

Aspects for implementation of data mining in gerontology and geriatrics

Current challenges facing the theory and practice in aging sciences require the use and development of new methods of investigation of observational and experimental data. This is associated with both the extensive development of the measuring and experimental basis of biological research and the progress in information support of studies in aging. As a result, large databases containing information on the state of health of vast groups of people who survived to an advanced age have been created. The combination of achievements in these directions make it possible to apply data mining methods that are successfully used for solving intricate tasks in economics, medical diagnostics, organization of the Internet, and other fields of science and technology for solving tasks in gerontology and geriatrics. This review provides some examples of the use of data mining methods in gerontology.

Data mining for feature selection in gene expression autism data

The paper presents application of data mining methods for recognizing the most significant genes and gene sequences (treated as features) stored in a dataset of gene expression microarray. The investigations are performed for autism data. Few chosen methods of feature selection have been applied and their results integrated in the final outcome. In this way we find the contents of small set of the most important genes associated with autism. They have been applied in the classification procedure aimed on recognition of autism from reference group members. The results of numerical experiments concerning selection of the most important genes and classification of the cases on the basis of the selected genes will be discussed. The main contribution of the paper is in developing the fusion system of the results of many selection approaches into the final set, most closely associated with autism. We have also proposed special procedure of estimating the number of highest rank genes used in classification procedure.

Integrating database knowledge and epidemiological design to improve the implementation of data mining methods that evaluate vaccine safety in large healthcare databases

AbstractLarge healthcare databases maintained by health plans have been widely used to conduct customized protocol‐based epidemiological safety studies as well as targeted routine sequential monitoring of suspected adverse events for newly licensed vaccines. These databases also offer a rich data source to discover vaccine‐related adverse events not known prior to licensure using data mining methods, but they remain relatively under‐utilized for this purpose. Initial safety applications of data mining methods using ‘big healthcare data’ are promising, but stronger integration of database expertize, epidemiological design, and statistical analysis strategies are needed to better leverage the available information, reduce bias, and improve reporting transparency. We enumerate major methodological challenges in mining large healthcare databases for vaccine safety research, describe existing strategies that have been used to address these issues, and identify opportunities for methodological advancements that emphasize the importance of adapting techniques used in customized protocol‐based vaccine safety assessments. Investment in such research methods and in the development of deeper collaborations between database safety experts and data mining methodologists has great potential to improve existing safety surveillance programs and further increase public confidence in the safety of newly licensed vaccines.

Data mining-based study on sub-mentally healthy state among residents in eight provinces and cities in China

ObjectiveTo apply data mining methods to research on the state of sub-mental health among residents in eight provinces and cities in China and to mine latent knowledge about many conditions through data mining and analysis of data on 3970 sub-mentallyhealthyindividualsselectedfrom13385 relevant questionnaires. MethodsThe strategic tree algorithm was used to identify the main manifestations of the state of sub-mental health. The backpropogation artificial neural network was used to analyze the main manifestations of sub-healthy mental states of three different degrees. A sub-mental health evaluation model was then established to achieve predictive evaluation results. ResultsUsing classifications from the Scale of Chinese Sub-healthy State, the main manifestations of sub-mental health selected using the strategic tree were F1101 (Do you lack peace of mind?), F1102 (Are you easily nervous when something comes up?), and F1002 (Do you often sigh?). The relative intensity of manifestations of sub-mental health was highest for F1101, followed by F1102, and then F1002. Through study of the neural network, better differentiation could be made between moderate and severe and between mild and severe states of sub-mental health. The differentiation between mild and moderate sub-mental health states was less apparent. Additionally, the sub-mental health state evaluation model, which could be used to predict states of sub-mental health of different individuals, was established using F1101, F1102, F1002, and the mental self-assessment total score. ConclusionsThe main manifestations of the state of sub-mental health can be discovered using data mining methods to research and analyze the latent laws and knowledge hidden in research evidence on the state of sub-mental health. The state of sub-mental health of different individuals can be rapidly predicted using the model established here. This can provide a basis for assessment and intervention for sub-mental health. It can also replace the relatively outdated approaches to research on sub-health in the technical era of information and digitization by combining the study of states of sub-mental health with information techniques and by further quantifying the relevant information.

Proactive Approach to Manufacturing Planning

Proposed concept of proactive manufacturing planning uses sets of knowledge to create plan. These sets of knowledge are gained from transformation of historical data of selected indicators. This concept uses the analysis of occurred events, which is done by applying data mining methods to known historical data. Results of analysis are then recorded into knowledge-based system for further use. Application of data mining techniques helps to find hidden relationships with high influence on final decision of planner. This concept aims to navigate planner during creation of real plans resulting from real situations. Unknown situations are modelled using simulation module.

Building an Integrated Water–Land Use Database for Defining Benchmarks, Conservation Targets, and User Clusters

AbstractWater utilities have large amounts of data at their disposal, which are seldom being used to their full potential. Integrating water billing records with land-use and demographic data organizes information and makes inherent correlations easier to understand, facilitating communication to stakeholders. This data was integrated for three Ontario (Canada) municipalities, Barrie, Guelph, and London. A summary tool was created, with proposed metrics and charts, that facilitates comparisons between cities, definition of benchmarks, and identification of targets for conservation. More than 60% of consumption in these cities is residential, and mostly lies below the Ontario average of 267 L/cap·day. Water user clusters were created through self-organizing maps, K-means, and hierarchical clustering, and selected according to their pseudo-F and Rand statistics. Users within the same or similar property codes were found to cluster together. The application of data-mining methods provides actionable informa...

A Brief survey of Data Mining Techniques Applied to Agricultural Data

As with many other sectors the amount of agriculture data based are increasing on a daily basis. However, the application of data mining methods and techniques to discover new insights or knowledge is a relatively a novel research area. In this paper we provide a brief review of a variety of Data Mining techniques that have been applied to model data from or about the agricultural domain. The Data Mining techniques applied on Agricultural data include k-means, bi clustering, k nearest neighbor, Neural Networks (NN) Support Vector Machine (SVM), Naive Bayes Classifier and Fuzzy cmeans. As can be seen the appropriateness of data mining techniques is to a certain extent determined by the different types of agricultural data or the problems being addressed. This survey summarize the application of data mining techniques and predictive modeling application in the agriculture field.

Context Management in Multiagent Intelligent Distance Learning Systems for Definition of Trainees’ Skills

The paper describes an approach to intelligent support of trainees in an intelligent distance learning system. The approach relies upon usage of context for assessment of trainees’ skills and upon application of data mining methods to accumulated trainees’ contexts for revealing of common difficulties and provision of personalized support for improvement of trainees’ knowledge skill. The approach has been tested via modeling of student agent, interface agent, accumulating agent, and data mining agent.

Approach to detect malware based on postionally dependent information

The problem of counteraction to malware remains quite severe, despite the emergence of more effective mechanisms for its identification, analysis, updating the database of its descriptions and detection rules. An important aspect of this problem is to find heuristic detection methods with better accuracy. This paper considers the application of data mining methods to create heuristic malware detectors. The described approach differs from existing by focusing on processing of static information, ensuring the formation of particular functional elements of an effective model to detect malicious executables. We implemented and studied a general methodology for creating detection system based on application of methods for selecting significant features and classification.

Application of data mining methods for classification and prediction of olive oil blends with other vegetable oils

The aim of this article is to study tree-based ensemble methods, new emerging modelling techniques, for authentication of samples of olive oil blends to check their suitability for classifying the samples according to the type of oil used for the blend as well as for predicting the amount of olive oil in the blend. The performance of these methods has been investigated in chromatographic fingerprint data of olive oil blends with other vegetable oils without needing either to identify or to quantify the chromatographic peaks. Different data mining methods-classification and regression trees, random forest and M5 rules-were tested for classification and prediction. In addition, these classification and regression tree approaches were also used for feature selection prior to modelling in order to reduce the number of attributes in the chromatogram. The good outcomes have shown that these methods allow one to obtain interpretable models with much more information than the traditional chemometric methods and provide valuable information for detecting which vegetable oil is mixed with olive oil and the percentage of oil used, with a single chromatogram.

Data Mining Open Source Tools - Review

Data Mining has become the area of growing significance because it helps in analyzing data from different perspectives and summarizing it into useful information and also data mining is defined to the analysis of observational data sets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner. And also researcher have to applying data mining methods and algorithms in many applications. whose development is driven by strong research interests as well as urgent practical, social, and economical needs. While the last few years knowledge discovery tools have been used mainly in research environments, sophisticated software products are now rapidly emerging. In this paper we have to give survey of most used and popular data mining tools of Clementine, Rapid miner, R, SAS enterprise miner and its features. These kind of data mining tools used for prediction and analyzing data mining process and using applications are education, learning environments, statistics and etc. Keywords: Data mining, clementine, Rapid miner, R, SAS Enterprise miner.

APPLYING DATA MINING METHODS IN ANALYSIS OF STRUCTURAL RELATIONSHIPS IN THE SYSTEM OF RANDOM PROCESSES

Abstract The aim of this paper is to present the possibilities of applying data mining techniques to the problem of the structural relationships analysis in the system of stationary random processes. In this paper, we will define the basic problem areas of analysis of structural relationships and we will show how to solve these problems by the methods of data mining. Individual solutions to selected problem areas will be demonstrated on a model which was designed in MATLAB. In the experiment, we will identify the existence and the nature of the process relationships in the system of stationary random processes based on the results of analyses.

A Comparative Study on Remote Tracking of Parkinson’s Disease Progression Using Data Mining Methods

In recent years, applications of data mining methods are become more popular in many fields of medical diagnosis and evaluations. The data mining methods are appropriate tools for discovering and extracting of available knowledge in medical databases. In this study, we divided 11 data mining algorithms into five groups which are applied to a dataset of patient’s clinical variables data with Parkinson’s Disease (PD) to study the disease progression. The dataset includes 22 properties of 42 people that all of our algorithms are applied to this dataset. The Decision Table with 0.9985 correlation coefficients has the best accuracy and Decision Stump with 0.7919 correlation coefficients has the lowest accuracy.

쌍대반응표면최적화의 방법론 및 응용 : A Literature Review

Dual response surface optimization (DRSO), inspired by Taguchi's philosophy, attempts to optimize the process mean and variability by using response surface methodology. Researches on DRSO were extensively done in 1990's and have been matured recently. This paper reviews the existing DRSO methods from the decision making perspective. More specifically, this paper classifies the existing DRSO methods based on the optimization criterion and the timing of preference articulation. Also, some of case studies are reviewed. Extension to multiresponse optimization, triple response surface optimization, and application of data mining method are suggested as future research issues.

Research on China Accounting Firms Competitiveness Based on Data Mining Methods

Accounting industry is an important part of advanced service industries, with rapid development for 30 years, accounting industry has become indispensable force to serve the development of economy and social. These days, how to strengthen own competitiveness has become the base for accounting firms to survive in market competition. This theses, based on the support of scale economy benefit theory, resource theory and core competency theory, applies data mining methods into analyzing indices of top 100 accounting firms in 2011, and uses improved algorithm to cluster accounting firms, verify clustering result with decision tree model, then puts forward strategies and suggestions to promote competition ability of accounting firms. This method verifies the application of combination model of cluster and decision tree, and avoids the limitation in analyzing with single model.

Application of data mining methods for link prediction in social networks

Using social networking services is becoming more popular day by day. Social network analysis views relationships in terms of nodes (people) and edges (links or connections—the relationship between the people). The websites of the social networks such as Facebook currently are among the most popular internet services just after giant portals such as Yahoo, MSN and search engines such as Google. One of the main problems in analyzing these networks is the prediction of relationships between people in the network. It is hard to find one method that can identify relation between people in the social network. The purpose of this paper is to forecast the friendship relation between individuals among a social network, especially the likelihood of a relation between an existing member with a new member. For this purpose, we used a few hypotheses to make the graph of relationships between members of social network, and we used the method of logistic regression to complete the graph. Test data from Flickr website are used to evaluate the proposed method. The results show that the method has achieved 99 % accuracy in prediction of friendship relationships.

A combination of variable selection and data mining techniques for high-dimensional statistical modelling

Variable selection is fundamental to statistical modelling in diverse fields of sciences. This paper deals with the problem of high-dimensional statistical modelling through the analysis of seismological data in Greece acquired during the years 1962-2003. The dataset consists of 10,333 observations and 11 factors, used to detect possible risk factors of large earthquakes. In our study, different statistical variable selection techniques are applied, while data mining techniques enable us to discover associations, meaningful patterns and rules. The statistical methods employed in this work were the non-concave penalised likelihood methods, SCAD, LASSO and Hard, the generalised linear logistic regression and the best subset variable selection. The applied data mining methods were three decision trees algorithms, the classification and regression tree (C&RT), the chi-square automatic interaction detection (CHAID) and the C5.0 algorithm. The way of identifying the significant variables in large datasets along with the performance of used techniques are also discussed.