A Generalized Rough Set Approach to Attribute Generalization in Data Mining

This paper presents a generalized method for updating approximations of a concept incrementally, which can be used as an effective tool to deal with dynamic attribute generalization. By combining this method and the LERS inductive learning algorithm, it also introduces a generalized quasi incremental algorithm for learning classification rules from data bases.

Domain-Oriented Data-Driven Data Mining Based on Rough Sets

Data mining (also known as Knowledge Discovery in Databases - KDD) is defined as the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. The aims and objectives of data mining are to discover knowledge of interest to user needs.Data mining is really a useful tool in many domains such as marketing, decision making, etc. However, some basic issues of data mining are ignored. What is data mining? What is the product of a data mining process? What are we doing in a data mining process? Is there any rule we should obey in a data mining process? In order to discover patterns and knowledge really interesting and actionable to the real world Zhang et al proposed a domain-driven human-machine-cooperated data mining process.Zhao and Yao proposed an interactive user-driven classification method using the granule network. In our work, we find that data mining is a kind of knowledge transforming process to transform knowledge from data format into symbol format. Thus, no new knowledge could be generated (born) in a data mining process. In a data mining process, knowledge is just transformed from data format, which is not understandable for human, into symbol format,which is understandable for human and easy to be used.It is similar to the process of translating a book from Chinese into English.In this translating process,the knowledge itself in the book should remain unchanged. What will be changed is the format of the knowledge only. That is, the knowledge in the English book should be kept the same as the knowledge in the Chinese one.Otherwise, there must be some mistakes in the translating proces, that is, we are transforming knowledge from one format into another format while not producing new knowledge in a data mining process. The knowledge is originally stored in data (data is a representation format of knowledge). Unfortunately, we can not read, understand, or use it, since we can not understand data. With this understanding of data mining, we proposed a data-driven knowledge acquisition method based on rough sets. It also improved the performance of classical knowledge acquisition methods. In fact, we also find that the domain-driven data mining and user-driven data mining do not conflict with our data-driven data mining. They could be integrated into domain-oriented data-driven data mining. It is just like the views of data base. Users with different views could look at different partial data of a data base. Thus, users with different tasks or objectives wish, or could discover different knowledge (partial knowledge) from the same data base. However, all these partial knowledge should be originally existed in the data base. So, a domain-oriented data-driven data mining method would help us to extract the knowledge which is really existed in a data base, and really interesting and actionable to the real world.

Rough set and radial basis function neural network based insulation data mining fault diagnosis for power transformer

Rough set (RS) and radial basis function neural network (RBFNN) based insulation data mining fault diagnosis for power transformer is proposed. On the one hand rough set is used as front of RBFNN to simplify the input of RBFNN and mine the rules. The mined rules whose “confidence” and “support” is higher than requirement are used to offer fault diagnosis service for power transformer directly. On the other hand the mining samples corresponding to the mined rule, whose “confidence and support” is lower than requirement, are used to be training samples set of RBFNN and these samples are clustered by rough set. The center of each clustering set is used to be center of radial basis function, i.e., as the hidden layer neuron. The RBFNN is structured with above base, which is used to diagnose the case that can not be diagnosed by mined simplified valuable rules based on rough set. The advantages and effectiveness of this method are verified by testing.

CALL FOR PAPERS Workshop on Intelligence and Security Informatics (WISI’06) in conjunction with the Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD’06)

Important Dates Submission due November 15, 2005 Notification of acceptance December 30, 2005 Camera-ready copy due January 10, 2006 Workshop Scope Intelligence and Security Informatics (ISI) can be broadly defined as the study of the development and use of advanced information technologies and systems for national and international security-related applications. The First and Second Symposiums on ISI were held in Tucson,Arizona,in 2003 and 2004,respectively. In 2005,the IEEE International Conference on ISI was held in Atlanta,Georgia. These ISI conferences have brought together academic researchers,law enforcement and intelligence experts,information technology consultant and practitioners to discuss their research and practice related to various ISI topics including ISI data management,data and text mining for ISI applications,terrorism informatics,deception detection,terrorist and criminal social network analysis,crime analysis,monitoring and surveillance,policy studies and evaluation,information assurance,among others. We continue this stream of ISI conferences by organizing the Workshop on Intelligence and Security Informatics (WISI’06) in conjunction with the Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD’06). WISI’06 will provide a stimulating forum for ISI researchers in Pacific Asia and other regions of the world to exchange ideas and report research progress. The workshop also welcomes contributions dealing with ISI challenges specific to the Pacific Asian region.

Function S-rough sets and mining-discovery of rough law in systems

Function S-rough sets （function singular rough sets） is defined on a -function equivalence class [u]. Function S-rough sets is the extension form of S-rough sets. By using the function S-rough sets, this paper gives rough law generation model of a-function equivalence class, discussion on law mining and law discovery in systems, and application of law mining and law discovery in communication system. Function S-rough sets is a new theory and method in law mining research.

Applied Approaches of Rough Set Theory to Web Mining

Rough set theory is a new soft computing tool, and has received much attention of researchers around the world. It can deal with incomplete and uncertain information. Now, it has been applied in many areas successfully. This paper introduces the basic concepts of rough set and discusses its applications in Web mining. In particular, some applications of rough set theory to intelligent information processing are emphasized.

Yarn Quality Prediction and Diagnosis Based on Rough Set and Knowledge-Based Artificial Neural Network

In the spinning process, some key process parameters( i. e.,raw material index inputs) have very strong relationship with the quality of finished products. The abnormal changes of these process parameters could result in various categories of faulty products. In this paper, a hybrid learning-based model was developed for on-line intelligent monitoring and diagnosis of the spinning process. In the proposed model, a knowledge-based artificial neural network( KBANN) was developed for monitoring the spinning process and recognizing faulty quality categories of yarn. In addition,a rough set( RS)-based rule extraction approach named RSRule was developed to discover the causal relationship between textile parameters and yarn quality. These extracted rules were applied in diagnosis of the spinning process, provided guidelines on improving yarn quality,and were used to construct KBANN. Experiments show that the proposed model significantly improve the learning efficiency, and its prediction precision is improved by about 5. 4% compared with the BP neural network model.

Web Mining Model Based on Rough Set Theory

Due to a great deal of valuable information contained in the Web log file, the result of Web mining can be used to enhance the decision making for electronic commerce (EC) operation and management. Because of ambiguous and abundance of the Web log file, the least decision making model based on rough set theory was presented for Web mining. And an example was given to explain the model. The model can predigest the decision making table, so that the least solution of the table can be acquired. According to the least solution, the corresponding decision for individual service can be made in sequence. Web mining based on rough set theory is also currently the original and particular method.

A New Approach for Knowledge Discovery in Distributed Databases Using Fragmented Data Storage Model

Since the early 1990, significant progress in database technology has provided new platform for emerging new dimensions of data engineering. New models were introduced to utilize the data sets stored in the new generations of databases. These models have a deep impact on evolving decision-support systems. But they suffer a variety of practical problems while accessing real-world data sources. Specifically a type of data storage model based on data distribution theory has been increasingly used in recent years by large-scale enterprises, while it is not compatible with existing decision-support models. This data storage model stores the data in different geographical sites where they are more regularly accessed. This leads to considerably less inter-site data transfer that can reduce data security issues in some circumstances and also significantly improve data manipulation transactions speed. The aim of this paper is to propose a new approach for supporting proactive decision-making that utilizes a workable data source management methodology. The new model can effectively organize and use complex data sources, even when they are distributed in different sites in a fragmented form. At the same time, the new model provides a very high level of intellectual management decision-support by intelligent use of the data collections through utilizing new smart methods in synthesizing useful knowledge. The results of an empirical study to evaluate the model are provided.

Risk Analysis Technique on Inconsistent Interview Big Data Based on Rough Set Approach

Rough set theory is relativly new to area of soft computing to handle the uncertain big data efficiently. It also provides a powerful way to calculate the importance degree of vague and uncertain big data to help in decision making. Risk assessment is very important for safe and reliable investment. Risk management involves assessing the risk sources and designing strategies and procedures to mitigate those risks to an acceptable level. In this paper, we emphasize on classification of different types of risk factors and find a simple and effective way to calculate the risk exposure.. The study uses rough set method to classify and judge the safety attributes related to investment policy. The method which based on intelligent knowledge accusation provides an innovative way for risk analysis. From this approach, we are able to calculate the significance of each factor and relative risk exposure based on the original data without assigning the weight subjectively.

An Overview of Data Mining and Knowledge Discovery

With massive amounts of data stored in databases, mining information and knowledge in databases has become an important issue in recent research. Researchers in many different fields have shown great interest in data mining and knowledge discovery in databases. Several emerging applications in information providing services, such as data warehousing and on-line services over the Internet, also call for various data mining and knowledge discovery techniques to understand user behavior better, to improve the service provided, and to increase the business opportunities. In response to such a demand, this article is to provide a comprehensive survey on the data mining and knowledge discovery techniques developed recently, and introduce some real application systems as well. In conclusion, this article also lists some problems and challenges for further research.

Knowledge discovery method for feature-decision level fusion of multiple classifiers

To improve the performance of the multiple classifier system, a new method of feature-decision level fusion is proposed based on knowledge discovery. In the new method, the base classifiers operate on different feature spaces and their types depend on different measures of between-class separability. The uncertainty measures corresponding to each output of each base classifier are induced from the established decision tables （DTs） in the form of mass function in the Dempster-Shafer theory （DST）. Furthermore, an effective fusion framework is built at the feature-decision level on the basis of a generalized rough set model and the DST. The experiment for the classification of hyperspectral remote sensing images shows that the performance of the classification can be improved by the proposed method compared with that of plurality voting （PV）.

Application Study of Rough Set in Remote Sensing for Crops

[Objective] This study aimed to improve classification accuracy of RS images using rough set theory in the growth of crops. [Method] Technique methods of data mining and knowledge discovery have been used. The development status of spatial data mining and knowledge discovery （SDMKD） is presented and data mining techniques in remote sensing were deeply analyzed. Then, SDMKD of TM image are researched using method of rough set, mainly including four methods （rough set, apriori algorithms, inductive learning, clustering）. [Result] The proposed method raises efficiency of land use and land reclaim. Based on the SDMKD, the characteristics of TM showed that the information after using rough set is more intensive than that of none. Especially, much better results can be gained while kinds of corps are less than five. [Conclusion] This study laid significant basis for further research on data mining in the growth of crops.

Call for Papers Special Issue of Tsinghua Science and Technology on Data Mining and Knowledge Discovery

Tsinghua Science and Technology is founded and published since 1996. It is an international academic journal sponsored by Tsinghua University and is published bimonthly. This journal aims at presenting the up-to-date scientific achievements in computer science, and other information technology fields. It is indexed by Ei and other abstracting and indexing services. From 2013, the journal commits to the open access at IEEE Xplore Digital Library.

Method and Application of Comprehensive Knowledge Discovery

This paper proposes the principle of comprehensive knowledge discovery. Unlike most of the current knowledge discovery methods, the comprehensive knowledge discovery considers both the spatial relations and attributes of spatial entities or objects. We introduce the theory of spatial knowledge expression system and some concepts including comprehensive knowledge discovery and spatial union information table (SUIT). In theory, SUIT records all information contained in the studied objects, but in reality, because of the complexity and varieties of spatial relations, only those factors of interest to us are selected. In order to find out the comprehensive knowledge from spatial databases, an efficient comprehensive knowledge discovery algorithm called recycled algorithm (RAR) is suggested.

Function S-Rough sets and its applications

Based on S-rough sets（singular rough sets）, this paper presents function S-rough sets （function singular rough sets）and its mathematical structures and features. Function S-rough sets has two forms： function one direction S-rough sets （function one direction singular rough sets） and function two direction S-rough sets （function two direction singular rough sets）. This paper advances the relationship theorem of function S-rough sets and S-rough sets. Function S-rough sets is the general form of S-rough sets, and S-rough sets is the special ease of function S-rough sets. In this paper, applications of function S-rough sets in rough law mining-discovery of system are given. Function S-rough sets is a new research direction of rough sets and rough system.

Knowledge Discovery in Data: A Case Study

It is common in industrial construction projects for data to be collected and discarded without being analyzed to extract useful knowledge. A proposed integrated methodology based on a five-step Knowledge Discovery in Data (KDD) model was developed to address this issue. The framework transfers existing multidimensional historical data from completed projects into useful knowledge for future projects. The model starts by understanding the problem domain, industrial construction projects. The second step is analyzing the problem data and its multiple dimensions. The target dataset is the labour resources data generated while managing industrial construction projects. The next step is developing the data collection model and prototype data ware-house. The data warehouse stores collected data in a ready-for-mining format and produces dynamic On Line Analytical Processing (OLAP) reports and graphs. Data was collected from a large western-Canadian structural steel fabricator to prove the applicability of the developed methodology. The proposed framework was applied to three different case studies to validate the applicability of the developed framework to real projects data.

Rough Mereology as a Tool for Knowledge Discovery and Reasoning in Intelligent Systems: A Survey

In this work, we present an account of our recent results on applications of rough mereology to problems of 1) knowledge granulation;2) granular preprocessing in knowledge discovery by means of decision rules;3) spatial reasoning in multi-agent systems in exemplary case of intelligent mobile robotics.

Disaster prediction of coal mine gas based on data mining

The technique of data mining was provided to predict gas disaster in view of the characteristics of coal mine gas disaster and feature knowledge based on gas disaster. The rough set theory was used to establish data mining model of gas disaster prediction, and rough set attributes relations was discussed in prediction model of gas disaster to supplement the shortages of rough intensive reduction method by using information en- tropy criteria.The effectiveness and practicality of data mining technology in the prediction of gas disaster is confirmed through practical application.