Preface This special issue of Fundamenta Informaticae (FI) contains a selection of papers presented initially RSKT is an international scientific conferences series that had been held successfully over last five years. The conferences are devoted to an integration of rough sets and knowledge technology, with an emphasis on both theoretical foundations and real-world applications. The eight papers included in this issue are substantially extended versions of conference papers. They cover various aspects of decision-theoretic rough sets (DTRS), fuzzy rough sets, attribute reduction and granular computing. Each paper went through at least two rounds of review. We thank all authors for submitting their best results and reviewers, for their careful, insightful and constructive reviews. The first paper, " Autonomous Knowledge-oriented Clustering Using Decision-Theoretic Rough Set Theory " by Hong Yu, Shuangshuang Chu and Dachun Yang, proposes an autonomous clustering method using the decision-theoretic rough set model based on a knowledge-oriented clustering framework. In order to get the initial knowledge-oriented clustering, the threshold values are produced autonomously based on semantics of clustering without human intervention. The risk of a clustering scheme based on the decision-theoretic rough set is studied by considering various loss functions. The second paper, " Modelling Multi-agent Three-way Decisions with Decision-Theoretic Rough Sets " by Xiaoping Yang and JingTao Yao, proposes a multiagent DTRS model and expresses it in the form of three-way decisions. The new model seeks for synthesized or consensus decisions when there are multiple decision preferences and criteria adopted by different agents. Various multi-agent DTRS models can be derived according to the conservative, aggressive and majority viewpoints based on the positive, negative and boundary regions made by each agent. These multi-agent decision regions are explained in terms of three-way decisions. The third paper, " A Multiple-category Classification Approach with Decision-Theoretic Rough Sets " by Dun Liu, Tianrui Li and Huaxiong Li, proposes a new two-stage approach to solve the multiple-category classification problems with DTRS by considering the levels of tolerance for errors and the cost of actions in a real decision procedure. The first stage is to change an m-category classification problem (m > 2) into m two-category classification problems, and to construct three types of decision regions: positive region, boundary region and negative region with respect to different states and actions by using DTRS. The second stage is to choose the best candidate classification in the positive region according to the minimum probability error criterion …