Distribution Reduction in Inconsistent Interval Ordered Information Systems Based on Dominance Relations

Attribute reduction is one of the basic contents in decision table. And it has been proved that computing the optimal attribute reduction is NP-complete. A lot of algorithms for the optimal attribute reduction were proposed in consistent decision table. But most decision tables are inconsistent in fact. In this paper, the judgment theorem with respect to maximum distribution reduction is obtained and the significance of attributes is defined in decision table, from which a polynomial heuristic algorithm for the optimal maximum distribution reduction is proposed. Finally, the experimental results show that this algorithm is effective and efficient.

Attribute reduction is a basic issue in knowledge representation and data mining. It simplifies an information system by discarding some redundant attributes. In this paper, we present a hybrid approach that combines the nature of variable neighbourhood search in the first phase with an iterated local search in the second phase that always accepts best solutions. The approach is tested over 13 well-known established datasets. The results demonstrate that the variable neighbourhood search approach is able to produce solutions that are competitive with those state-of-the-art techniques from the literature in terms of minimal reducts.

An improved attribute reduction scheme with covering based rough sets

A systematic study on attribute reduction with rough sets based on general binary relations

An incremental approach to attribute reduction from dynamic incomplete decision systems in rough set theory

Attribute reduction plays an important role in knowledge discovery and data mining. Confronted with data characterized by the interval and missing values in many data analysis tasks, it is interesting to research the attribute reduction for interval-valued data with missing values. Uncertainty measures can supply efficient viewpoints, which help us to disclose the substantive characteristics of such data. Therefore, this paper addresses the attribute reduction problem based on uncertainty measure for interval-valued data with missing values. At first, an uncertainty measure is provided for measuring candidate attributes, and then an efficient attribute reduction algorithm is developed for the interval-valued data with missing values. To improve the efficiency of attribute reduction, the objects that fall within the positive region are deleted from the whole object set in the process of selecting attributes. Finally, experimental results demonstrate that the proposed algorithm can find a subset of attributes in much shorter time than existing attribute reduction algorithms without losing the classification performance.

Reduction is one of the key problem in rough set theory due to its applications in data mining, rule induction, classification, etc.. In this paper the reductions for a set-valued decision information system(DIS) are studied. The judgment theorem and the discernibility matrix of the generalized decision reduct in a set-valued DIS are given, and the relationships among the generalized decision reduct and alternative types of knowledge reduction in set-valued DIS are investigated. It is proved that the reduct in the consistent set-valued DIS is equivalent to the generalized decision reduct, and the possible reduct in the inconsistent set-valued DIS is equivalent to the generalized decision reduct. The judgment theorem and the discernibility matrix associated with the possible reduct are also established.

This chapter provides a research frame for geographic information scientists to study the integration of geospatial data mining and knowledge discovery. It examines the current state of Data mining (DM) and Knowledge discovery data (KDD) technology, identifies special needs for geospatial DM and KDD, and outlines research challenges and the significance to national research needs. The chapter explores research frontiers in geographic knowledge discovery briefly and propose a research agenda to highlight short-term, midterm, and long-term objectives. The University Consortium for Geographic Information Science seeks to facilitate a multidisciplinary research effort on the development of geospatial DM/KDD science and technology. Geographic data has unique properties that require special consideration and techniques. Geographic information exists within highly dimensioned geographic measurement frameworks. Knowledge-based Geographic Information Science attempts to build higher-level geographic knowledge into digital geographic databases for analyzing complex phenomena. The development of DM and knowledge discovery tools must be supported by a solid geographic foundation.

Twofold fuzzy sets and rough sets—Some issues in knowledge representation

Attribute reduction (feature selection) has become an important challenge in areas of pattern recognition, machine learning, data mining and knowledge discovery. Based on attribute reduction, one can extract fuzzy decision rules from a fuzzy decision table. As consistency is one of several criteria for evaluating the decision performance of a decision-rule set, in this paper, we devote to present a consistency-preserving attribute reduction in fuzzy rough set framework. Through constructing the membership function of an object, we first introduce a consistency measure to assess the consistencies of a fuzzy target set and a fuzzy decision table, which underlies a foundation for attribute reduction algorithm. Then, we derive two attribute significance measures based on the proposed consistency measure and design a forward greedy algorithm (ARBC) for attribute reduction from both numerical and nominal data sets. Numerical experiments show the validity of the proposed algorithm from search strategy and heuristic function in the meaning of consistency. Number of the selected features is the least for a given threshold of consistency measure.

Enhancing our knowledge of the complexities of cities in order to empower ourselves to make more informed decisions has always been a challenge for urban research. Recent developments in large-scale computing, together with the new techniques and automated tools for data collection and analysis are opening up promising opportunities for addressing this problem. The main motivation that served as the driving force behind this research is how these developments may contribute to urban data analysis. On this basis, the thesis focuses on urban data analysis in order to search for findings that can enhance our knowledge of urban environments, using the generic process of knowledge discovery using data mining. A knowledge discovery process based on data mining is a fully automated or semi-automated process which involves the application of computational tools and techniques to explore the “previously unknown, and potentially useful information” (Witten a Frank, 2005) hidden in large and often complex and multi-dimensional databases. This information can be obtained in the form of correlations amongst variables, data groupings (classes and clusters) or more complex hypotheses (probabilistic rules of co-occurrence, performance vectors of prediction models etc.). This research targets researchers and practitioners working in the field of urban studies who are interested in quantitative/computational approaches to urban data analysis and specifically aims to engage the interest of architects, urban designers and planners who do not have a background in statistics or in using data mining methods in their work.

A novel method for attribute reduction of covering decision systems

Attribute reduction is an important process in many fields, such as knowledge discovery, data mining, machine learning, pattern recognition and so on. However, totally labeled data are quite hard to obtain in real life. Thus, we often have to face the situation that not all the data have been associated with labels in advance. Due to the coexistence of labeled and unlabeled data, attribute reduction problem for partially labeled data becomes more complex and challenging. Many scholars have devoted themselves to solve this problem in the past few years. But current algorithms for partially labeled data are not efficient enough in terms of time complexity. To address this issue, we propose a hypergraph model, where two types of induced hypergraphs are designed from partially labeled decision systems. Then, a fast algorithm based on low-complexity heuristics is raised to compute the minimum vertex cover of a hypergraph. Finally, we propose two types of hypergraph models-based attribute reduction algorithms for partially labeled decision systems. Experimental results on broadly used data sets show that the feasibility and efficiency of our proposed algorithms.

The increasing use of computers in all the areas of human activities is resulting in huge collections of digital data. Databases are common everywhere and are used as repositories of every kind of data. Knowledge discovery techniques and tools are used today to analyze those very large data sets to identify interesting patterns and trends in them. When data is maintained over geographically distributed sites the computational power of distributed and parallel systems can be exploited for knowledge discovery in databases. In this scenario the Grid can provide an effective computational support for distributed knowledge discovery on large data sets. To this purpose we designed a system called Knowledge Grid This chapter describes the Knowledge Grid architecture and discusses some related systems and models recently proposed for knowledge discovery on Grids. The chapter presents also how to design and implement distributed data mining applications by using the Knowledge Grid tools starting from searching Grid resources, composing software and data elements, and executing the resulting application on a Grid.

Knowledge discovery is a compute and data intensive process that allows for finding patterns, trends, and models in large datasets. The Grid can be effectively exploited for deploying knowledge discovery applications because of the high-performance it can offer and its distributed infrastructure. For effective use of Grids in knowledge discovery, the development of middleware is critical to support data management, data transfer, data mining and knowledge representation. To such purpose, we designed the Knowledge Grid, a high-level environment providing for Grid-based knowledge discovery tools and services. Such services allow users to create and manage complex knowledge discovery applications, composed as workflows that integrate data sources and data mining tools provided as distributed Grid services. This chapter describes the Knowledge Grid architecture and describes how its components can be used to design and implement distributed knowledge discovery applications. Then, the chapter describes how the Knowledge Grid services can be made accessible using the Open Grid Services Architecture (OGSA) model.