Knowledge Reduction in Set-Valued Decision Information System

Knowledge reduction is an important issue when dealing with huge amounts of data. Rough set theory provides two fundamental concepts to deal with this special problem: attribute reduction and core, where attribute reduction is the key problem. There are a lot of methods available for attribute reduction, but very little work has been done for attribute reduction for the cases of deleting and modifying. Therefore, in this paper, we introduce a new updating method based on discernibility matrix for attribute reduction for the case of deleting. When a few old objects are deleted from a decision information system, a core and a new attribute reduction can be obtained quickly because it only deletes a few old rows and columns, or inserts a few rows and updates corresponding columns when updating the discernibility matrix.

This paper deals with knowledge reduction in generalized consistent decision formal contexts. The concept of a generalized consistent decision formal context is first introduced. Its equivalent definitions are also examined. The judgement theorem and discernibility matrix which are helpful for computing reducts are then established, from which we can obtain an approach to knowledge reduction in generalized consistent decision formal contexts.

Rough sets serves as a tool for data analysis and knowledge discovery from data databases. Attribute reduction is a basic issue in knowledge representation and data mining. This paper deals with distribution reduction in an inconsistent interval ordered information systems. The distribution reduction and maximum distribution reduction are proposed in inconsistent interval ordered information systems. Moreover, properties and relationship between them are discussed. Furthermore, judgement theorem and discernibility matrix are obtained, from which approaches to distribution reductions can be provided in inconsistent interval ordered information systems.

Decision information system based on rough set is an important processing form in data mining. Attribute reduction is one of the most critical issues in rough set theory. Chen et.al. proposed an attribute reduction algorithm for decision information system based on the covering generalized rough sets. In this paper, we first point out that Chen's algorithm applies to the fifth, the sixth and the seventh type of covering generalized rough sets. Then we establish the equivalent relationship between covering and the reflexive and transitive relation. Moreover, we prove that attribute reduction based on covering in this paper is a special case of the one based on general binary relation proposed by Wang et.al. As a result, a new attribute reduction algorithm with lower time complexity is designed. With these discussion, the research provides theoretical and practical significance.

SMA Negeri 7 Kota Bengkulu is one of the State Senior High SchoolsState Senior High School located in Bengkulu City which has a commitment to in developing student potential. So far, data processing of student grades for each subjects have used e-report cards and the report card data can be exported to a Microsoft Excel file. exported into a Microsoft Excel file. However, the student grade data is data is often only stored as an archive without any further analysis to find out to find out information about patterns or trends in student achievement. At where this can help the school in providing guidance and optimizing the academic potential of each student at SMA Negeri 7. optimize the academic potential of each student at SMA Negeri 7 Bengkulu City based on the level of student achievement. Bengkulu City based on student achievement levels. Application of data mining in grouping data grades of SMA Negeri 7 Bengkulu City students using the K-Means Clustering Method is built desktop-based with the Visual Basic .Net programming language. Visual Basic .Net programming language, The application of data mining in grouping data in clustering student grades of SMA Negeri 7 Bengkulu City is carried out per student subject with subject value attributes and attendance percentage. The application of data mining in clustering student grade data of SMA Negeri 7 Bengkulu City can help in knowing the level of achievement of students of SMA Negeri 7 Bengkulu City. Bengkulu City can help in knowing the level of student achievement in each subject and can be used as an evaluation material to improve the level of student achievement in each subject. subjects and can be used as evaluation material to improve the achievement of students who are at low achievement levels. who are at a low level of achievement.

Knowledge reduction is one of challenging problems in rough set theory, many static knowledge reduction algorithms have been proposed, however, since the objects in the actual information system are often changed, these static algorithms are computationally time-consuming. In order to overcome this shortcoming, the concept of simplified information system is introduced, and it is proved that knowledge reduction based on the simplified discernibility matrix is equivalent to that based on skowron’s discernibility matrix, On this condition, an efficient incremental updating algorithm for knowledge reduction is designed, which can be used to improve the efficiency of knowledge reduction, when a new object is added to information system. Finally, Theoretical analysis and example results show that illustrate the efficiency and feasibility of the algorithm.

Decision-making in information systems often involves uncertainty and imprecision. Traditional methods, such as those based on classical Rough Set Theory and original Pawlak’s model may struggle to handle such complexities and various data set class types. Keeping in mind the importance of similarity or dissimilarity measures and applications in data mining, medical diagnosis, decision making, and pattern recognition. This study proposes a novel approach to estimative similarity or dissimilarity degree membership calculation with fuzzy decision-making systems, leveraging symmetry relationships. Our method aims to enhance decision-making accuracy and robustness by considering the inherent uncertainties present in real-world data. Experimental results for selected application of dataset represents a hypothetical medical diagnosis scenario demonstrate the superiority of our approach compared to existing techniques, making it a promising tool for various applications in information systems. By calculating estimative membership degrees based on these relationships and similarity or dissimilarity, our approach offers a more nuanced understanding of decision boundaries and uncertainties.

Attribute selection in an information system is one of the important applications of rough set theory. This paper studies attribute selection for heterogeneous data based on information entropy. We first define information entropy in an information system with heterogeneous data and then put forward the notions of joint information entropy, conditional information entropy and mutual information entropy in a decision information system with heterogeneous data. We apply information entropy to perform attribute selection in a decision information system with heterogeneous data. We propose two attribute selection algorithms based on information entropy. Finally, we make experimental analysis and comparisons to illustrate the feasibility and efficiency of the proposed algorithms.

Knowledge management is an application that includes human, processes and technology for managing information and knowledge resources in an organization. With its variety of sophisticated techniques, data mining appears within the knowledge management process of knowledge creation in particular. Organizations make use of knowledge management application and data mining in multiple functional aspects such as manufacturing, finance and marketing. With respect to marketing, organizational products, marketing decisions are very critical to their profit generation. This paper reviews the application of knowledge management and data mining in marketing decisions. The objective of the study is to infer the application of knowledge management and data mining in marketing decision making. Using the scoping review technique, a number of 30 scholarly articles on knowledge management, data mining and marketing decision have been gathered and reviewed. The analysis of the retrieved articles found that clustering was the most frequent technique applied in marketing decision. It is also identified that sale and cash registers are marketing dimensions regularly reported to apply data mining. Scholars have also acknowledged that marketing decisions have effectively applied data mining.

Attribute reduction plays an important role in rough set theory. It is an important application in data mining. In this paper, we focus on discussing the relation between set covering and attribute reduction in rough set theory. Based on the equivalence between minimal set covering and minimal attribute reduction sets, attribute reduction graph (ARG) is constructed. A novel algorithm to find the minimal attribute reduction sets, which is based on combination of binary discernibility matrix and graph theory is proposed in this paper. This algorithm demonstrates its efficiency and feasibility by an example.

Many interesting real-life mining applications rely on modeling data as sequences of discrete multi-attribute records. Mining models for network intrusion detection view data as sequences of TCP/IP packets. Text information extraction systems model the input text as a sequence of words and delimiters. Customer data mining applications profile buying habits of customers as a sequence of items purchased. In computational biology, DNA, RNA and protein data are all best modeled as sequences. Classifying, clustering and characterizing such sequence data presents interesting issues in feature engineering, discretization and pattern discovery. In this seminar we will review techniques ranging from item set counting, MDL-based discretization and Markov modeling to perform various supervised and unsupervised pattern discovery tasks on sequences. We will present case studies from network intrusion detection and DNA sequence mining to illustrate these techniques. Sunita Sarawagi researches in the fields of databases, data mining, machine learning and data warehousing. She is a member of the faculty at IIT Bombay. Prior to that she was a research staff member at IBM Almaden Research Center. She got her PhD in databases from the University of California at Berkeley.

The construction of the economic management information system ensures the flow and sharing of information, business coordination, and scientific decision-making between economic functions. Therefore, project construction is absolutely necessary and urgent. Project management has achieved significant results in terms of shortening project time and reducing management costs. Information system is an important model information structure in artificial intelligence. This paper focuses on the design of a fusion method for economic management problems based on decision information systems. This paper summarizes the value of the whole project through the research on the project management of the economic management information system. The results of this study show that when the threshold is fixed, the information granularity Gtheta and rough entropy (Er) theta decrease monotonically with increasing attribute subset cardinality. Meanwhile, θ-information granularity Etheta and θ-information entropy Htheta monotonically increase with the increase of attribute subset cardinality, which indicates that when attribute subset cardinality increases, the uncertainty of the information system decreases.

This paper strives to outline the use and applications of data mining in the health sector, tailor-made for the land-locked Lesotho, in Southern Africa. The concepts discussed in this paper can easily be modified to fit several industries; higher education, the legal sector, the retail and telecommunications industries and the insurance sector. Healthcare management can be improved immensely with the use of data mining. The goal of our study is to discover patterns in the HIV/AIDS treatment among patients in Lesotho.Close monitoring of the disease progression is the most vital aspect as far as curbing fatalities is concerned.Using data mining tools, detailed patient information and treatment records can be used to determine if the course of treatment will work in order to keep a patient alive for a longer period of time.

Reduction of knowledge is one of the important issues in the research of rough set theory. It has been proven that the search for an optimal (minimal) reduction of decision table is NP-hard problem. Thus people have been trying to search for more efficient heuristic algorithms to get an approximate reduction of decision table all the time. Every heuristic must depends on a measure of the significance of attributes in decision table, because it is the key heuristic information to determine which attribute is reducible in decision table. It was analyzed by examples in this paper that the current four commonly used methods all have some drawbacks. Using them to a heuristic leads to wrong results. Then a new definition for measuring the significance of attributes in decision table is given based on discernibility matrix. The new definition can discern the difference of significance of no-core attributes what cannot be discerned or misled by current commonly used methods. The new definition is very useful in the practice of data mining and reduction of knowledge

Context: The Big Data phenomenon has imposed maturity on companies regarding the exploration of their data, as a prerogative to obtain valuable insights into their clients and the power of analysis to guide decision-making processes. Therefore, a general approach that describes how to extract knowledge for the execution of the business strategy needs to be established. Purpose: The purpose of this research paper is to introduce and evaluate the implementation of a process for the experimental development of Data Mining (DM), AI and Data Science applications aligned with the strategic planning. Method: A case study with the proposed process was conducted in a federal educational institution. Results: The results generated evidence showing that it is possible to integrate a strategic alignment approach, an experimental method, and a methodology for the development of DM applications. Conclusion: Data Mining (DM) and Data Science (DS) applications also present the risks of other Information Systems, and the adoption of strategy-driven and scientific method processes are critical success factors. Moreover, it was possible to conclude that the application of the scientific method was facilitated, besides being an important tool to ensure the quality, reproducibility and transparency of intelligent applications. In conclusion, the process needs to be mapped to foment and guide the strategic alignment.