In this study, we propose the method creating fuzzy control rules based on experiential knowledge. With the advent of data mining techniques, a new way is provided to create fuzzy control rules. With the introduction of incremental data mining techniques, fuzzy control rules can be updated dynamically in time on the condition that massive new data is producing.

Professor Pascal Poncelet talk was focused on analysis of data associate to social network. In his presentation, Professor Poncelet summarized several techniques through knowledge discovery on databases process. In this talk, doctor Roche outlined last tendencies in text mining task. Doctor Hacid presented a complete overview concerning techniques associated to explote social web data. Techniqu...

XML has gained popularity for information representation, exchange and retrieval. As the XML material becomes more abundant, the ability to gain knowledge from XML sources decreases due to their heterogeneity and structural irregularity. The use of data mining techniques becomes essential to improve XML document handling. This paper discusses the capabilities and the process of applying data mi...

Agent-Based Knowledge Discovery provides a new technique for performing data-mining over distributed databases. By combining techniques from Distributed AI and Machine Learning, software agents equipped with learning algorithms mine local databases. These agents then co-operate to integrate the knowledge obtained, before presenting the results to the user. We are currently exploring the use of ...

Data mining is aimed at the extraction of interesting (i.e., nontrivial, implicit, previously unknown, and potentially useful) patterns or knowledge from huge amounts of data. In order to make data mining manageable, data mining has to be database centered. Yet, data mining goes beyond the traditional realm of database techniques; in particular, reasoning methods developed from machine learning...

Semantic Web Mining is the outcome of two new and fast developing domains: Semantic Web and Data Mining. The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation. Data Mining is the nontrivial process of identifying valid, previously unknown, potentially useful patterns in data. Semantic W...

In this paper we argue that data mining can make a significant contribution to knowledge management initiatives. We use two studies to show how data mining can make the difference during the knowledge management process. First, we describe how data mining was used as part of the knowledge management initiative in a major Portuguese telecommunications company. We demonstrate how implementation o...

In this paper, we propose an automatic and autonomous methodology to discover taxonomies of terms from the Web and represent retrieved web documents into a meaningful organization. Moreover, a new method for lexicalizations and synonyms discovery is also introduced. The obtained results can be very useful for easing the access to web resources of any medical domain or creating ontological repre...

The real life intelligent applications such as agents, expert systems, dialog understanding systems, weather forecasting systems, robotics etc. mainly focus on commonsense knowledge And basically these works on the knowledgebase which contains large amount of commonsense knowledge. The main intention of this work is to create a commonsense knowledgebase by using an effective methodology to retr...

Many healthcare leaders find themselves overwhelmed with data, but lack the information they need to make right decisions. Knowledge Discovery in Databases (KDD) can help organizations turn their data into information. Organizations that take advantage of KDD techniques will find that they can lower the healthcare costs while improving healthcare quality by using fast and better clinical decisi...