Empirical analysis of rough set categorical clustering techniques based on rough purity and value set

Clustering a set of objects into homogeneous groups is a fundamental operation in data mining. Recently, attention has been put on categorical data clustering, where data objects are made up of non-numerical attributes. The implementation of several existing categorical clustering techniques is challenging as some are unable to handle uncertainty and others have stability issues. In the process of dealing with categorical data and handling uncertainty, the rough set theory has become well-established mechanism in a wide variety of applications including databases. The recent techniques such as Information-Theoretic Dependency Roughness (ITDR), Maximum Dependency Attribute (MDA) and Maximum Significance Attribute (MSA) outperformed their predecessor approaches like Bi-Clustering (BC), Total Roughness (TR), Min-Min Roughness (MMR), and standard-deviation roughness (SDR). This work explores the limitations and issues of ITDR, MDA and MSA techniques on data sets where these techniques fails to select or faces difficulty in selecting their best clustering attribute. Accordingly, two alternative techniques named Rough Purity Approach (RPA) and Maximum Value Attribute (MVA) are proposed. The novelty of both proposed approaches is that, the RPA presents a new uncertainty definition based on purity of rough relational data base whereas, the MVA unlike other rough set theory techniques uses the domain knowledge such as value set combined with number of clusters (NoC). To show the significance, mathematical and theoretical basis for proposed approaches, several propositions are illustrated. Moreover, the recent rough categorical techniques like MDA, MSA, ITDR and classical clustering technique like simple K-mean are used for comparison and the results are presented in tabular and graphical forms. For experiments, data sets from previously utilized research cases, a real supply base management (SBM) data set and UCI repository are utilized. The results reveal significant improvement by proposed techniques for categorical clustering in terms of purity (21%), entropy (9%), accuracy (16%), rough accuracy (11%), iterations (99%) and time (93%). vi

Health-related quality of life as measured with EQ-5D among populations with and without specific chronic conditions: A population-based survey in Shaanxi province, China

© 2013 Tan et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Introduction: The aim of this study was to examine health-related quality of life (HRQoL) as measured by EQ-5D and to investigate the influence of chronic conditions and other risk factors on HRQoL based on a distributed sample located in Shaanxi Province, China. Methods: A multi-stage stratified cluster sampling method was performed to select subjects. EQ-5D was employed to measure the HRQoL. The likelihood that individuals with selected chronic diseases would report any problem in the EQ-5D dimensions was calculated and tested relative to that of each of the two reference groups. Multivariable linear regression models were used to investigate factors associated with EQ VAS. Results: The most frequently reported problems involved pain/discomfort (8.8%) and anxiety/depression (7.6%). Nearly half of the respondents who reported problems in any of the five dimensions were chronic patients. Higher EQ VAS scores were associated with the male gender, higher level of education, employment, younger age, an urban area of residence, access to free medical service and higher levels of physical activity. Except for anemia, all the selected chronic diseases were indicative of a negative EQ VAS score. The three leading risk factors were cerebrovascular disease, cancer and mental disease. Increases in age, number of chronic conditions and frequency of physical activity were found to have a gradient effect. Conclusion: The results of the present work add to the volume of knowledge regarding population health status in this area, apart from the known health status using mortality and morbidity data. Medical, policy, social and individual attention should be given to the management of chronic diseases and improvement of HRQoL. Longitudinal studies must be performed to monitor changes in HRQoL and to permit evaluation of the outcomes of chronic disease intervention programs. © 2013 Tan et al.National Nature Science Foundation (No. 8107239

A review of offshore wind turbines: global added capacity, monopile structure foundations stresses and deflection

Offshore wind power is now a significant source of clean renewable energy. The paper summarizes the key findings from recent energy studies on the growth in offshore wind power\u27s added capacity to the global energy system. The review paper referred to parts of the configuration of offshore wind turbines (OWTs) and their supporting structures, and focused on the monopole support structure due to its importance in building offshore wind farms and studying and discussing the most published research related to the mono support structure and its response to wind loads and wave loads affecting them. Recent studies have varied between numerical analysis research, research and master\u27s theses and doctoral theses on calculating stresses on offshore wind turbines. Due to the importance of the topic, studies on computational fluid dynamics (CFD), which have developed greatly in recent years, have been reviewed (simulation and simulation research). Hybrids (RTHS) and experimental research. The paper concluded with the conclusion that the interest in experimental studies and research close to the conditions of marine turbines is through the construction of special laboratories that include advanced equipment with quantitative measurements, with high technical standards and good reliability, developments of simulation tools of various forms in order to approach efficient and low-cost design

A Content Based Filtering Approach for the Automatic Tuning of Compiler Optimizations

Recently a large number of compiler conversions have been implemented to optimize programs.  A comprehensive exploration of all possible sequences of optimization  is not practical because the search space is huge considering the large number of compiler optimizations passes. In addition, predicting the effectiveness of these optimizations is not an easy task.  In this work, the suggested approach offers automatic tuning of compiler optimization sequences in place of manually tuning by recommended optimization sequences based on program features. Techniques inspired from the Recommendation System (RS) field to provide a solution to the autotuning of compiler optimizations problem. Content Based filtering method is finding a group of programs that are closest to the unseen program based on the similarity of their features. Then the best optimization sequences for these programs are recommended to the unseen one. Two versions of the CBF method, with and without rate value are presented. The approach is evaluated using three benchmark suites PolyBench, Shootout, and Stanford, including 50 different programs and using LLVM (Low Level Virtual Machine) compiler passes down Linux Ubuntu. Results obtained showed that such method is superior to the standard level of optimization -O3 of LLVM compiler in improving the execution time  by an average of 9.3 % for CBF without rate, 13.7% for CBF with rate

Implementasi Algoritma MCL (Markov Cluster Algorithm) pada Pengelompokan Berita Digital dengan Representasi Graph

Berita digital yang akan terus bertambah dan berkembang memunculkan permasalahan baru untuk memodelkan data berita digital yang tersimpan dalam database lebih mudah untuk dipahami dan diambil beberapa informasi penting yang menyeluruh. Untuk memudahkan pengolahan informasi dalam database tersebut diperlukan suatu model dan metode tertentu untuk mengelompokkan berita-berita tersebut berdasarkan kedekatan dan karakteristiknya satu sama lain. Menggunakan model graph database dan metode graph clustering dengan algoritma MCL (Markov Cluster Algorithm) dapat mempermudah pengolahan informasi dengan cara mengidentifikasi karakteristik tiap vertex dalam graph sehingga akan membentuk kelompok-kelompok vertex dengan label terterntu. Dalam proses pengidentifikasian kelompok graph, satu dokumen berita digital akan disimpan ke dalam satu vertex yang akan dihubungkan dengan vertex lainnya yang memiliki kesamaan kategori berita. Proses expand dan inflate matriks akan menjadi proses utama dalam pengelompokan berita digital yang telah ditransformasi ke dalam model graph database dimana expand bertujuan untuk memunculkan edge baru yang dianggap perlu dan menghapus edge lama yang dianggap tidak dipelukan dalam graph. Sedangkan proses inflate bertujuan untuk memperkuat edge yang telah kuat dan memperlemah edge yang telah lemah. Sehingga dalam pengelompokan berita digital ini proses inflate matriks sangat berpengaruh dalam execution time algoritma MCL dan jumlah cluster yang akan terbentuk

Automatic panoramic medical image stitching improvement based on feature-based approach

Clarification publications in the medical field are very important in making doctors the right decision by finding evidence to support his decision, therefore, the importance of collecting medical images and combining them with multiple overlapping areas of the same scene is important. This (processing, multimedia images and their medical applications) is very difficult. Our system proposed in this paper is applicable to the medical field of scoliosis and other Rib cage. The problem is the narrow vision of the X-ray machine and the lack of a large picture in one frame, the best solution is to combine more than one x-ray image into one panoramic image, our proposed method relies on in light of feature-based methodology by Circle (Oriented-FAST and Rotated-BRIEF). The rapid wave approach is used to describe the feature through the use of BRIEF technology, the standard that has been adopted in our technology to describe the performance of the planning is based on the processing time and image quality created. The purpose of using the feature extraction approach in our technology is to obtain a high-resolution panoramic image plus short processing time, the results that we were able to obtain, according to the experimental results applied, resulted in ORB image quality and recording time