Knowledge discovery and data mining deal with the task of finding useful information and especially rules in unstructured data. Most knowledge discovery approaches associate conditional probabilities to discovered rules in order to specify their strength. In this paper, we propose a qualitative approach to knowledge discovery. We do so by abstracting from actual probabilities to qualitative inf...

* " Universal knowledge " also can be named as " pervasive knowledge / information " or " ubiquitous knowledge / information ". Abstract: To overcome the shortcomings of knowledge discovered by traditional data mining methods, I argue that we should follow the paradigm shift from " traditional data mining " to " wisdom mining " in the era of big data, and propose Universal Knowledge Discovery f...

Uninformed or blind search, which processes and evaluates all nodes of a search space in the worst case, is not realistic for extracting knowledge from large data sets because of time constraints that are closely related to the dimension of the data. Generally, the search space increases exponentially with problem size, thereby limiting the size of problems that can realistically be solved usin...

With the flood of information on the Web, Web mining is a new research issue which draws great interest from many communities. Currently, there is no agreement about Web mining yet. It needs more discussion among researchers in order to define what it is exactly. Meanwhile, the development of Web mining system will promote its research in turn. In this paper we present a preliminary discussion ...

Knowledge reduction, includes attribute reduction and value reduction, is an important topic in rough set literature. It is also closely relevant to other fields, such as machine learning and data mining. In this paper, an algorithm called TWI-SQUEEZE is proposed. It is so named because it can find a reduct, or an irreducible attribute subset that maintains certainty of classification, after tw...

Over the last years, ubiquitous computing has started to create a new world of small, heterogeneous, and distributed devices that have the ability to sense, to communicate and interact in ad hoc or sensor networks and peer2peer systems. These large scale distributed systems have in many cases to interact in real-time with their users. Knowledge Discovery in ubiquitous environments (KDubiq) is a...

Knowledge Discovery in Databases (KDD) is a relatively new field of research. Interest generated by KDD has been huge. International companies are looking to KDD techniques to maximise profit and enhance decision support. The aim of this paper is to provide an overview and understanding of the ideas, techniques and applications found within this field.

• Mining Uncertain Heterogenous Information Networks Scalable and intelligent methods to handle the challenges of uncertainty, heterogeneity, and large volumes and/or low bandwidth, focused on three major data sources in military. network + spatial-temporal data + streams. • Domain Integration for Decision Support Integrate knowledge learned from networks, spatial-temporal data and streams all ...

Within large corpora of texts, Knowledge-Rich Contexts (KRCs) are a subset of sentences containing information that would be valuable to a human for the construction of a knowledge base. The entry point to the discovery of KRCs is the automatic identification of Knowledge Patterns (KPs) which are indicative of semantic relations. Machine readable dictionary serves as our starting point for inve...

Reading a book is also kind of better solution when you have no enough money or time to get your own adventure. This is one of the reasons we show the scientific data mining and knowledge discovery principles and foundations as your friend in spending the time. For more representative collections, this book not only offers it's strategically book resource. It can be a good friend, really good f...