This paper presents a study on Web-based learning support systems that is enhanced with two major subsystems: a Web-based
learning game and a learning-oriented Web search. The Internet and theWeb may be considered as a first resource for
students seeking for information and help. However, much of the information available online is not related to the course
contents or is wrong in the worse case. The search subsystem aims to provide students with precise, relative and adaptable
documents about certain courses or classes. Therefore, students do not have to spend time to verify the relationship of
documents to the class. The learning game subsystem stimulates students to study, enables students to review their studies
and to perform self-evaluation through a Web-based learning game such as a treasure hunt game. During the challenge
and entertaining learning and evaluation process, it is hoped that students will eventually understand and master the course
concepts easily. The goal of developing such a system is to provide students with an efficient and effective learning
environment.
We present a mathematical model for a dynamic Delphi survey method which takes advantages of Web technology. A comparative study on the performance of the conventional Delphi method and the dynamic Delphi instrument is conducted. It is suggested that a dynamic Delphi survey may form a consensus quickly. However, the result may not be robust due to the judgement leaking issues.
Current intrusion detection techniques mainly focus on discovering
abnormal system events in computer networks and distributed
communication systems. Clustering techniques are normally utilized
to determine a possible attack. Due to the uncertainty nature of
intrusions, fuzzy sets play an important role in recognizing
dangerous events and reducing false alarms level. This paper
proposes a dynamic approach that tries to discover known or
unknown intrusion patterns. A dynamic fuzzy boundary is developed
from labelled data for different levels of security needs. Using a
set of experiment, we show the applicability of the approach.
One of the major drawbacks or challenges of neural network models
is that these models can not explain what they have done. Extracting rules from trained neural networks is one of the solutions for understanding the networks. However, what we should do with these extracted rules remains a research question. This paper tries to address issues on effectively and efficiently utilizing extracted rules or knowledge.
The XML is a new standard for data representation and exchange on
the Internet. There are studies on XML query languages as well as
XML algebras in literature. However, attention has not been paid
to research on XML algebras for data mining due to partially the
fact that there is no widely accepted definition of XML mining tasks. This paper tries to examine the XML mining tasks and provide guidelines to design XML algebras for data mining. Some summarization and comparison have been done to existing XML algebras. We argue that by adding additional operators for mining tasks, XML algebras may work well for data mining with XML documents.
The main stream of research in data mining (or knowledge discovery in databases) focuses on algorithms and automatic or semi-automatic processes for discovering knowledge hidden in data. In this paper, we adopt a more general and goal oriented view of data mining. Data mining is regarded as a field of study covering the theories, methodologies, techniques, and activities with the goal of discovering new and useful knowledge. One of its objectives is to design and implement data mining systems. A miner solves problems of data mining manually, or semi-automatically by using such systems. However, there is a lack of studies on how to assist a miner in solving data mining problems. From the experiences and lessons of decision support systems, we introduce the concept of data mining support systems (DMSS). We draw an analogy between the field of decision-making and the field of data mining, and between the role of a manager and the role of a data miner. A DMSS is an active and highly interactive computer system that assists data mining activities. The needs and the basic features of DMSS are discussed.
In this paper, we discuss the potential applications of data
mining techniques for the design of Web based information retrieval
support systems (IRSS). In particular, we apply clustering methods
for the granulation of different entities involved in IRSS. Two
types of granulations, single-level and multi-level granulations,
are investigated. Issues of document space granulation, query space
granulation, term space granulation, and retrieval results granulation are studied in detail. It is demonstrated that each different granulation supports a different user task.
This paper addresses some fundamental issues related to
the foundations of data mining. It is argued that there is an urgent
need for formal and mathematical modeling of data mining. A
formal framework provides a solid basis for a systematic study of
many fundamental issues, such as representations and
interpretations of primitive notions of data mining, data mining
algorithms, explanations and applications of data mining results.
A multi-level framework is proposed for modeling data mining
based on results from many related fields. Formal concepts
are adopted as the primitive notion. A concept is jointly defined as a pair consisting of the intension and the extension of the concept,
namely, a formula in a certain language and a subset of the
universe. An object satisfies the formula of a concept if the
object has the properties as specified by the formula, and the
object belongs to the extension of the concept. Rules are used
to describe relationships between concepts. A rule is expressed
in terms of the intensions of the two concepts and is interpreted
in terms of the extensions of the concepts. Several different
types of rules are investigated. The usefulness and meaningfulness
of discovered knowledge are examined using a utility model and
an explanation model.
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