In today’s changing world huge amount of data is generated and transferred frequently. Although the data is sometimes static but most commonly it is dynamic and transactional. New data that is being generated is getting constantly added to the old/existing data. To discover the knowledge from this incremental data, one approach is to run the algorithm repeatedly for the modified data sets which...

The theory of rough set, proposed by Pawlak, provides a formal tool for knowledge discovery from imprecise and incomplete data. Dimensionality reduction (RED) is a crucial problem for rough set based data mining. Unfortunately, it proves to be a NP-hard problem. Many reduction algorithms based on heuristic information have been developed, but they are all based on Boolean space {0,1}m. In this ...

Rough Sets Theory has opened new trends for the development of the Incomplete Information Theory. Inside this one, the notion of reduct is a very significant one, but to obtain a reduct in a decision system is an expensive computing process although very important in data analysis and knowledge discovery. Because of this, it has been necessary the development of different variants to calculate ...

In this paper, we introduce a new type of reducts called the λ-Fuzzy-Reduct, where the fuzzy similarity relation is constructed by means of cosine-distances of decision vectors and the parameter λ is used to tune the similarity precision level. The λ-Fuzzy-Reduct can eliminate harsh requirements of the distribution reduct, and it is more flexible than the maximum distribution reduct, the tradit...

Over recent years, the research of attribute reduction for general decision systems and, in particular, consistent tables has attracted great attention from computer science community due to emerge big data. It been known that, a table, we can derive polynomial time complexity algorithm finding reduct. In addition, redundant properties also be done time. However, all reduct sets table is proble...

Every day, we are observing that a wide-ranging amount of data and information is being stored every moment. Mostly the information is dynamic and transactional, and each time the data is being updated from time to time. Getting knowledge from this kind of huge and dynamic data is really becoming tough task with respect to time and efficiency. Fortunately we are served with the intelligent proc...

Image retrieval system is developing driven by the explosive growing of multimedia data. However the oneshot result can’t fit the human perception perfection. Relevance feedback is utilized to improve the retrieval performance. In this paper, a new relevance feedback algorithm-ARFRS is proposed. This algorithm is composed of two parts: pre-processing part and classification part. The pre-proces...

We study the relationships among structural methods for identifying and solving tractable classes of Constraint Satisfaction Problems (CSPs). In particular, we first answer a long-standing question about the notion of biconnected components applied to an "optimal" reduct of the dual constraint-graph, by showing that this notion is in fact equivalent to the hinge decomposition method. Then, we g...