Applying Data Mining in Score Analysis

The explosion of very large databases has created extraordinary opportunities for monitoring, analyzing and predicting global economical, geographical, demographic, medical, political, and other processes in the world. Statistical analysis and data mining techniques have emerged for these purposes. Data mining is the process of discovering previously unknown but potentially useful patterns, rules, or associations from huge quantity of data. Data mining can be performed on different data repositories such as relational databases, data warehouses, transactional databases, sequence databases, spatial databases, spatio-temporal databases, and text databases, etc. Typically, data mining functionalities can be classified into two categories: descriptive and predictive. Descriptive mining tasks aim at characterizing the general properties of the data in the databases, while predictive mining tasks perform inherence on the current data in order to make prediction in future.KeywordsRoot Mean Square ErrorData MiningAssociation RuleIntrusion DetectionAnomaly DetectionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The data mining has been performed for the aerodynamic design optimization result of two-stage-to-orbit reusable launch vehicle flyback booster wing. Three data mining techniques were used such as self-organizing map, functional analysis of variance, and rough set theory. The optimization problem had four aerodynamic objective functions and 71 design variables regarding wing shape. The optimization obtained the result as the hypothetical design database with 302 all solutions including the 102 non-dominated solutions. Consequently, the knowledge in the design space was acquired regarding the correlation between objective functions, and the influence of the design variables to the objective function, for non-dominated and all evaluated solutions, respectively. The features of three data mining techniques were revealed. Although the combination among three techniques discovered detailed design knowledge, self-organizing map was especially a key technique for knowledge discovery. Moreover, design knowledge from all solutions conserved the information from non-dominated solutions. Data mining was essential to solve multi-objective optimization problem.

S-rough sets (singular rough sets) theory was proposed by improving Z.Pawlak rough sets theory. S-rough set has dynamic characteristics, since it is defined by R -element equivalence class[x] which has dynamic characteristic. The concept of rough data was presented by employing S-rough sets, and rough data has dynamic characteristic, furthermore, the rough data law generation is given. Based on the concepts above, the generation theorem and the reversion theorem of rough data law, the recognition principle of rough data law, and the discernible theorem of rough data law were given, finally the applications of rough data law in risk investment system are presented.

This chapter describes issues of database design for databases that provide representations that allow rough set data. We first present the definition of a rough relational database. A database can be designed by Entity-Relationship modeling or by defining the functional dependencies for the system. We describe rough set E-R models and rough functional dependencies. Based on the rough functional dependencies we provide the various related rough normal forms used in a database schema design. Finally, we discuss issues of database security and the use of rough spatial data in a rough relational database.

The LNCS journal Transactions on Rough Sets is devoted to the entire spectrum of rough sets related issues, from logical and mathematical foundations, through all aspects of rough set theory and its applications, such as data mining, knowledge discovery, and intelligent information processing, to relations between rough sets and other approaches to uncertainty, vagueness, and incompleteness, such as fuzzy sets and theory of evidence. Volume XVI includes extensions of papers from the Rough Sets and Knowledge Technology Conference which was held in Banff, Canada, in October 2011. In addition this book contains a long paper based on a PhD thesis. The papers cover both theory and applications of rough, fuzzy and near sets. They offer a continuation of a number of research streams which have grown out of the seminal work by Zdzislaw Pawlak during the first decade of the 21st century.

This paper presents a study were the Rough Set Theory and Data Mining Technique are applied to the electrical power system. The Data Mining technique classifies the system operation in four possible states: normal, alert, emergency (emergency I and emergency II). The states, that correspond to the normal state can be classified as secure and insecure the remaining ones. In this security studies, the overloads in transmition lines and the violation of the voltage limits are used to classify and rank these contingencies. This technique was applied to the 118IEEE busbar test power network and the results obtained are analyzed. Finally, some conclusions that provide a valuable contribution to the understanding of the power system security analysis are pointed out.

The rough sets theory can effectively express inexact or uncertain knowledge ,and is good at acquiring the knowledge from data. It can reason from uncertain and incomplete experience or knowledge.Therefore ,the application of the rough sets theory is more and more welcome in data mining (i.e.knowledge discovery). Taking the survey sample database for purehase intentions of car users as a example,The paper detailedly introduces a way to apply the rough sets theory in data mining as follow:firstly,according to acquired original sample information , make knowledge categories for car users and their key attentions for purehase intentions ; secondly, construct a original decision table determining their key attentions for purehase intentions as condition attributes, and car users as decision attribute; thirdly, according to Adding method relative to the knowledge core and value core of attributes, find respectively its relative knowledge reduction sets and value reduction sets; finally, from simplified decision tables for the minimum relative knowledge reduction sets, extract decision rules. Compared with other data mining , besides dealing with data the way does not require any prior information,and can acquire useful knowledge only by analyzing ,judging and mining original sample information.

With the requirements of the power load predicting accuracy becoming higher and higher, and the actual power load is also a complex nonlinear system limited by various uncertain factors. Given only considering the influence of a single model on the load forecasting, the predicting accuracy is absolutely not high under the comprehensive effects of multiple factors. Ensuring to make the electrical model satisfy the requirements, there is access to improving the accuracy of load predicting results through analysis of the relationship between load and multiple factors in power load forecasting. This paper combines the rough set theory with information fusion, firstly a decision table of attributes is established on the basis of historical datum, for the purpose of simplifying the attribute index in mining to obtain important information. Following step is to rank the importance of the factors influencing the load according to the degree of effect, with grouping these sorted factors into attributes combination and using the same kind of load forecasting method to predict each combination's own results, and finally all groups of the predicting results are integrated by fusion, where then final predicting result can be obtained. The results of the algorithmic example show that groups of predicting results integrated based on information fusion theory are better than the results got through any casual single attribute combination. And fusion results are a combined action of a variety of related factors, enhancing the favorable trend, where at the same time it has reduced the uncertainty brought by multiple factor comprehensive effect. Based on information fusion the predicting results are helpful to reduce the prediction error, which is feasible and effective. With the explosive growth on the amount of in today's information age, the wave of data is coming, fast and furious. The era of big proposes a new challenge for the development in electricity industry. With the advance of electricity industry, intelligence, automation and interaction will bring many datum casing electrical big generation. Due to the numerous index of power load forecasting, different goals of all kinds of power load forecasting, even with the same kind of theory, the forecast model to predict no matter from the form, content or effect also vary widely. The load in the medium and long-term prediction of power system is greatly influenced by economy, society and meteorology factors and other uncertainties, etc. How to extract the key factor from multiple factors and reduce the results' uncertainty arising from the comprehensive effect of multiple factors, and improving the precision has been becoming a serious problem to be solved. Aiming at these problems, the paper based on the literature (8) proposing medium-and long- term load forecasting based on partial least squares regression analysis, an more optimized and advanced method is put forward to improve accuracy of prediction. The rough set theory and information fusion are introduced into the model, which respectively advance pre-processing and optimize the integration of multiple sets of results after prediction, in order to reduce uncertainty caused by varieties factors affecting and improve accuracy of forecasting results. II. THE ROUGH SET THEORY In the era of big data, on account of the characteristics on grid volume, which is large amount, variety type, and more speed, the traditional attribute simplified methods have been unable to complete the pre-processing on electrical big data. To this end the paper proposes a method on power big simplified processing based on cloud computing technology. The rough set theory which offers a new way for mining, is a new mathematical tool to deal with uncertain and imprecise problems. In this paper, rough sets method is used in power load forecasting to analyze the dependence of load on each factor and the importance of various properties in condition attribute set. In the decision system R={U, A}, the rough set divides A(attribute) into two categories C and D, where C is condition attribute, D is decision attribute, and U is domain. The ternary collection R={U,C,D} is known as a decision-making system. An important application of rough set in the analysis is to find the dependence among attributes from a known decision system R={U,C,D}. The dependency is defined as follows.

The trends of data mining on healthcare data for improving medical services have increased because of the electronic healthcare record(EHR) system, which collects a massive amount of data on a daily basis. In the current scenario, hospital maintains its EHR system and stores the detailed information of patients. Data mining for healthcare improvement requires the data from all the EHR systems located at a different location to be stored at the central data mining server. Collection of healthcare data at some untrusted central data mining server raises privacy threats. Healthcare data contains patients’ private information and sharing this information for data mining creates privacy issues. Most of the previous research either focused on k-anonymity technique which causes information loss and decreases data mining accuracy or privacy preserving data mining which is focused on only specific data mining technique. We adopt source anonymous technique as privacy preserving scheme and present a novel scheme for healthcare data collection and mining in this paper. Our scheme collects data from all EHR systems without any information loss and stores at a single central data mining server, also ensuring privacy is preserved. Central data mining server helps to analyze the collected data with different data mining techniques (Association rule mining, Classification, Clustering, etc.) without the involvement of EHR systems. Our scheme is collusion resilient against central data mining server and EHR systems. Theoretical and experimental analysis show the efficiency of our scheme in terms of computation and communication cost. The experimental results using Heart disease dataset show the advantage to EHR systems using the proposed approach in terms of disease prediction accuracy.

Discovering useful models capturing regularities of natural phenomena or complex systems until recently was almost entirely limited to finding formulae fitting empirical data. This worked relatively well in physics, theoretical mechanics, and other areas of science and engineering. However, in social sciences, market research, medicine, pharmacy, molecular biology, learning and perception, and in many other areas, the complexity of the natural processes and their common lack of analytical smoothness almost totally exclude the use of standard tools of mathematics for the purpose of data-based modeling. A fundamentally different approach is needed in those areas. The availability of fast data processors creates new possibilities in that respect. This need for alternative approaches to modeling from data was recognized some time ago by researchers working in the areas of neural nets, inductive learning, rough sets, and, more recently, data mining. The empirical models in the form of data-based structures of decision tables or rules play similar roles to formulas in classical analytical modeling. Such models can be analyzed, interpreted, and optimized using methods of rough set theory.

There are various data mining techniques that are frequently used for the mining of vital patterns embedded within bulk data. These techniques include neural network, regression analysis, rough set theory, Bayesian network, decision trees, and so on. In this research, a novel data mining technique, genetically guided cluster based fuzzy decision tree (GCFDT), is introduced for the mining task. In order to test the efficacy of GCFDT, it is employed for building the predictive process models of reactive ion etching (RIE) with the aid of optical emission spectroscopy (OES) signals. This model endeavors to predict the wafer surface conditions for the new incoming set of process parameters. OES is an efficient tool for monitoring plasma emission intensity. In contrast with the C-fuzzy decision tree where granules are devolved through fuzzy clustering here, granulation is practised through genetically guided fuzzy clustering. The growth of the tree is governed by expanding the node having highest diversity. The results obtained by employing CGFDT in RIE process modeling reveal that it dominates both the traditional C-fuzzy decision trees and C4.5 decision trees in terms of both the accuracy and compactness.

Chapter 12 - Application, functionality, and security issues of data mining techniques in healthcare informatics

Granular computing is a title of methodologies, techniques, and devices that make use of granules in the process of problem solving. The idea of granular computing has shown in a lot of areas, and it is likely playing a more and more important position in data mining. Rough set theory and fuzzy set theory, as two very important patterns of granular computing, are frequently used to process vague information in data mining. Combining rough set theory and fuzzy set theory, a flexible method is for processing imperfect information systems.

Decision rules mining is an important technique in machine learning and data mining, it has been studied intensively during the past few years. However, most existing algorithms are based on flat data tables, from which sets of decision rules mined may be very large for massive data sets. Such sets of rules are not easily understandable and really useful for users. Moreover, too many rules may lead to over-fitting. Thus, a method of decision rules mining from different abstract levels was provided in this chapter, which aims to improve the efficiency of decision rules mining by combining the hierarchical structure of multidimensional model and the techniques of rough set theory. Our algorithm for decision rules mining follows the so called separate-and-conquer strategy. Namely, certain rules were mined beginning from the most abstract level, and supporting sets of those certain rules were removed from the universe, then drill down to the next level to recursively mine other certain rules which supporting sets are included in the remaining objects until no objects remain in the universe or getting to the primitive level. So this algorithm can output some generalized rules with different degree of generalization.Request access from your librarian to read this chapter's full text.

In this paper, we study the application of fuzzy set theory, genetic algorithms and rough set theory techniques to the control of the rectifier. A fuzzy adaptive control scheme with the aid of rough set theory via genetic algorithms (GAs) finding the center parameters in place of the classical control is proposed. On the one hand, genetic algorithm gets optimal parameters of an accurate domain model, which tunes the scaling factors of fuzzy adaptive control but is hardly established in a non-liner system. On the other hand, fuzzy adaptive control deals with the dynamics and complexity of responses from rectifier of a HVDC system in the operation points, by adjusting its control parameters with the aid of rough tuner adaptively. Our study includes a brief introduction to fuzzy control and rough set algorithms, both theory and application. We also evaluate the performance of rough fuzzy control by simulation in the paper. The focus of our experiments is on the constant current control in rectifier side of HVDC system. The result shows there are many improvements offered by the fuzzy control scheme based on rough set theory in comparison with the only fuzzy control scheme in the rectifier side of HVDC system.