Earticle

Integration of Entropy and fuzzy group decision-making method was applied into the earthquake emergency priority problem and difficulty, which provided a practical and scientific assistant decision-making mode. Through the demonstration of index system and its hierarchy of earthquake emergency priority, fuzzy group decision-making was mapped into earthquake emergency, fuzzy decision-making matrix was erected for earthquake emergency priority, and the priority of earthquake emergency was determined with the Improved Entropy weights. Finally, example showed that model could present guidance for the earthquake emergency plans and local decision-making operations.

To standardize and construct an inventory database for a water supply system, an inheritance tree is constituted from Level 1 to 5 for specification of a classification structure for the water supply system. Level 1 is specified based on systems while Level 2 to 5 are specified to classify water supply systems by considering a structure’s role, function and type for each system. Also, the property information for asset management of each structure is systematcially investigated and written on a form to enable standardization of an inventory database. The property information is divided into asset classification information, asset location spatial information, asset specification information and asset management-related information, and the definitions, types, units and descriptions of the examined data are systematcially organized

In order to improve the search result and low evolution speed, and avoid the tendency towards stagnation and falling into the local optimum of ant colony optimization(ACO) in solving the complex function, the traditional ant colony optimization algorithm is analyzed in detail, an improved ant colony optimization(IWSMACO) algorithm based on information weight factor and supervisory mechanism is proposed in this paper. In the proposed IWSMACO algorithm, the information weight factor is added to the path selection and pheromone adjustment mechanisms in order to dynamically adjust path selection probability and randomly select the behavior rules for further intelligentializing the ant colony. The supervisory mechanism added the dynamic convergence criterion of supervisory distance and used the optimal pheromone update strategy to self-adaptively select the excellent ants for updating the pheromone trails, and improve the solution qualities of each iteration, better guide the later ants for learning. Finally, the proposed IWSMACO algorithm is carried out by 12 TSP instances. The simulation experiment results show that the proposed IWSMACO algorithm can not only avoid falling into the local optimum, but also enhance the convergence speed. And it takes on remarkable optimized ability and higher search accuracy.

This paper studies notions and approaches to attribute reduction in property oriented concept lattices of formal contexts and formal decision contexts based on congruence relations. Firstly, dependence space based on the property oriented concept lattice is researched to obtain the relationship among property oriented concept lattices and the corresponding congruence relations. Then notions of attribute reduction is defined for formal contexts and decision contexts to find minimal attribute subsets which can preserve all congruence classes determined by the original attribute set, and also keep all property oriented extents and their original hierarchy in the property oriented concept lattice. Finally, approaches of discernibility matrix are presented to calculate all attribute reducts.

In this age of information, which is available in different forms and these information play imperative part to enhance our knowledge base and so our social life. Information presentation is as important as information itself because to interpret the inside knowledge and interaction with it makes it more effective. This study investigates legend navigation interactive technique for comparative exploration of descriptive data mining results in order to communicate the insight information quickly, easily and in well understandable form. The legend navigation interactive mechanism is applied to two visualization techniques (column charts and bar charts) by performing descriptive data mining task on published Amazon dataset. The experimentation is done with 41 volunteers, selected by simple random sampling technique. The interactive technique is comprehensively analyzed in both visualization techniques considering visualization features. Equate the results with drill down interactive mechanism and discussed the utility of mechanism based on visualization features. The legend navigation approach and drill down interactive mechanism showed better performance in column chart comparatively.

Data mining retrieves knowledge information from larger amounts of data. Clustering is an assemble of similar objects in to one class and dissimilar objects in to another class. When designing clustering ensemble on large dimensional data space, both time and space requirements for processing may be overinflated. This tends to impose feature selection methods to remove redundant features and handle the noise data. There are filter, wrapper and hybrid methods in feature selection. This paper shows a tour on types of feature selection techniques and numbers of experiments are conducted to compare feature selection techniques using different datasets with R tool, which gives better technique for clustering ensemble design.

To solve the clustering algorithm based on grid density on uncertain data stream in adjustment cycle for clustering omissions, the paper proposed an algorithm, named GCUDS, to cluster uncertain data steam using grid structure. The concept of the data trend degree was defined to describe the grade of a data point belonging to some grid unit and the defect of information loss around grid units was removed in the GCUDS algorithm. The GCUDS algorithm obtained better results of clustering and higher time efficiency than other algorithms over uncertain data stream, through improving the traditional online clustering framework and maintaining three buffers of micro-cluster. Experimental results showed that the GCUDS algorithm could effectively cluster in different shape database and outperform existing methods in clustering quality and efficiency.

Sequence is an important mission and performance in such repair decision-making operation for the earthquake-damaged transport infrastructures. Through introducing Set Pair Analysis ideology, the decision-making model was established for earthquake repair sequence of transport infrastructures. Then, the SPA comparing space was built, the association identity, contrary and difference degree parameters were calculated for repair projects, the relative SPA proximity was determined for each repair project, as well as the repair sequence was given. Meanwhile, the integration of Efficiency Coefficient method and Information Entropy to determine the index weights, improved the reliability of repair sequence. Finally, Case showed a typical evaluation index system and model application, which provided a scientific, simple and suitable method on the repair priority decision-making operation for the earthquake-damaged transport infrastructures.

Technological advancements have evolved into several application domains to solve various problems. One such technological area is Data Mining. It has shown its significance and potential in health care industries to serve as a guiding and decision making component. Its potential in unveiling new trends in health care organizations has proved its importance for all people associated with this area. It is the most important and encouraging area of research which have the motive to find out the information from large data set. Advance researches in data mining had made it a key player in health care field. Good analytical techniques are of utmost requirement for detecting precious information lying hidden in health industry data. This survey paper presents the importance and usefulness of different Data mining techniques such as classification, clustering, Decision Tree, Naive Bayes etc. in health domain. Here the study and comparison is done of different data mining techniques used for prediction of cancer disease from clinical dataset with different accuracy.

According to the teaching and reform practice experience of database course for economics and management major, the paper makes a comparative analysis of the database courses of the universities in china and foreign, in terms of textbook, teaching content, teaching method, homework form, examination method and so on. In this paper, some new ideas about system and content of database course based on the “big course” and “big task” are put forward, and some improvements on teaching method have been discussed.

The SSGAMLP(Small Set Genetic Algorithm Multilayer Perceptron) model helps individual evolution by group evolution. With respect to the MLP, it has better generalization, it can get unknown feature expressions of more possibilities. The model still exist many problems need to be solved. The number of nodes in the hidden layers and the population size of MLP has a great influence on the performance of SSGAMLP. So this paper focuses on the optimization of that two parameters on SSGAMLP. In this paper, the models of several different experiments are designed. By comparing the experimental data, the relationship between the parameter selection and the model performance is obtained.

This paper presents a data-driven methodology to disambiguate a query by suggesting relevant subcategories within a specific domain. This is achieved by finding correlations between the user’s search history and the context of the current search keyword. We apply automatic categorization on each query to identify a list of categories which can describe the query given. To predict the categories of a user input query, we employed machine learning algorithms. We present the preliminary evaluation results and conclude with future work.

Sentiment analysis is shown significant and indispensable status in hot topic, public-opinion poll, knowledge acquiring and recommended goods fields, which is the fundamental work for natural language processing. This paper proposes an approach to build the Tibetan sentiment dictionary and to calculate the sentiment value base on the Tibetan semantic relations. We test our approach on experimental corpus crawled from Sina weibo and the experimental results demonstrate good performance on Tibetan language.

Colleges and universities should make full use of modern information technology to improve the teaching mode based on computer aided English teaching mode. The new teaching model should be based on modern information technology, especially network technology, so that English teaching is not subject to the restrictions of time and place, and toward the development of personalized, autonomous learning. In this paper, we analyze network topology structure and put forward an improved RTRL learning algorithm. Based on the empirical analysis, the result shows that with the increase of teaching time, the result of the experimental class is more obvious, so that the new teaching model promotes the improvement of students' autonomous learning ability. In conclusion, cultivating students' autonomous learning ability in a new and diversified foreign language teaching environment has become the core of College English teaching reform.

K-Means is a famous partition based clustering algorithm. Various extensions of K-Means have been proposed depending on the type of datasets being handled. Popular ones include K-Modes for categorical data and K-Prototype for mixed numerical and categorical data. The K-Means and its extensions suffer from one major limitation that is dependency on prior input of number of clusters K. Sometimes it becomes practically impossible to correctly estimate the optimum number of clusters in advance. Various ways have been suggested in literature to overcome this limitation for numerical data. But for categorical and mixed data work is still in progress. In this paper, we introduce a new algorithm based on the K-Means that takes mixed dataset as an input and generates appropriate number of clusters on the run using MapReduce programming style. The new algorithm not only overcomes the limitation of providing the value of K initially but also reduces the computation time using MapReduce framework.

Support vector machine (SVM) is an important algorithm in data mining; it can transform the nonlinear classification problem into a linear classification problem by increasing the dimension of the data. The author points out the shortcomings of the traditional data analysis methods, and puts forward the method of complex simulation data analysis based on distributed SVM data mining algorithm. In the empirical part, through construct the evaluation index system of the school sports balanced development mode, the results show that the primary indicators of the sports balanced development are resource allocation(0.3774), school physical education process(0.2781), school physical education results(0.2450), and school sports social environment(0.1000).Overall, the balanced development of school physical education is a long and gradual process, sports evaluation index system also needs to be constantly updated and revised.

With the development of network and big data technology, how to dig out the effective information from massive data is the problem that we are facing. Traditional data mining algorithms in the face of big data, it is powerless. With the ability of parallel computing, data is processed on several machines, so the efficiency of data processing will be greatly improved. In this paper, the author analyzes the parallel data mining algorithm application in the evaluation of college counselors' psychological guidance ability, and puts forward the strategies of improving the ability of college counselors' psychological guidance. Based on the statistical analysis, the result shows that there were no significant differences in gender, but in the context of communication, female were better than male. The male instructors are superior to female instructors in the understanding ability of the observation and diagnosis and the plan of action. Obviously, training is a very effective way to improve the ability of College Counselors' psychological counseling, and it should be promoted and applied.

Streaming data are potentially infinite sequence of incoming data at very high speed and may evolve over the time. This causes several challenges in mining large scale high speed data streams in real time. Hence, this field has gained a lot of attention of researchers in previous years. This paper discusses various challenges associated with mining such data streams. Several available stream data mining algorithms of classification and clustering are specified along with their key features and significance. Also, the significant performance evaluation measures relevant in streaming data classification and clustering are explained and their comparative significance is discussed. The paper illustrates various streaming data computation platforms that are developed and discusses each of them chronologically along with their major capabilities. This paper clearly specifies the potential research directions open in high speed large scale data stream mining from algorithmic, evolving nature and performance evaluation measurement point of view. Finally, Massive Online Analysis (MOA) framework is used as a use case to show the result of key streaming data classification and clustering algorithms on the sample benchmark dataset and their performances are critically compared and analyzed based on the performance evaluation parameters specific to streaming data mining.

With the development of information technology, technology mining technology can help users to find the needed information accurately and efficiently. In this paper, the author makes factors analysis of professional growth of innovative talents based on data mining technology. Knowledge innovation is the starting point of scientific and technological innovation, and the development of innovative talents is the most important and the scarcest resource for enterprises. By analyzing the professional growth of innovative talents, we construct the evaluation index system of innovative talents. The conclusion proves that the balance between supply and demand of enterprise and personal professional growth is the important factor to promote organizational technology progress and the career development of employees.

Reliable data sharing and long-term data archiving and reuse are becoming very important in global cooperative research. Concomitantly, many types of global data-based research have been conducted on the Long-Term Ecological Research (LTER) network, with the objective of getting it to respond effectively to future changes in ecology, environment, and climate, by monitoring long-term ecological and environmental data. Korean National Long-Term Ecological Research (KNLTER), however, lacks a system for performing data collection, management, curation, and publication, and therefore global cooperative research through global data sharing is difficult in Korea. In this paper, we analyze one of the best practices link models, the TERN network, and the global data-sharing trend in the LTER area. Further, we propose a link model and necessary technologies for KNLTER and suggest a possible future direction for KNLTER.