Abstract: A learning content management system (LCMS) is an
environment to support web-based learning content development.
Primary function of the system is to manage the learning process as
well as to generate content customized to meet a unique requirement
of each learner. Among the available supporting tools offered by
several vendors, we propose to enhance the LCMS functionality to
individualize the presented content with the induction ability. Our
induction technique is based on rough set theory. The induced rules
are intended to be the supportive knowledge for guiding the content
flow planning. They can also be used as decision rules to help
content developers on managing content delivered to individual
learner.
Abstract: The problem of frequent pattern discovery is defined
as the process of searching for patterns such as sets of features or items that appear in data frequently. Finding such frequent patterns
has become an important data mining task because it reveals associations, correlations, and many other interesting relationships
hidden in a database. Most of the proposed frequent pattern mining
algorithms have been implemented with imperative programming
languages. Such paradigm is inefficient when set of patterns is large
and the frequent pattern is long. We suggest a high-level declarative
style of programming apply to the problem of frequent pattern
discovery. We consider two languages: Haskell and Prolog. Our
intuitive idea is that the problem of finding frequent patterns should
be efficiently and concisely implemented via a declarative paradigm
since pattern matching is a fundamental feature supported by most
functional languages and Prolog. Our frequent pattern mining
implementation using the Haskell and Prolog languages confirms our
hypothesis about conciseness of the program. The comparative
performance studies on line-of-code, speed and memory usage of
declarative versus imperative programming have been reported in the
paper.
Abstract: Frequent pattern discovery over data stream is a hard
problem because a continuously generated nature of stream does not
allow a revisit on each data element. Furthermore, pattern discovery
process must be fast to produce timely results. Based on these
requirements, we propose an approximate approach to tackle the
problem of discovering frequent patterns over continuous stream.
Our approximation algorithm is intended to be applied to process a
stream prior to the pattern discovery process. The results of
approximate frequent pattern discovery have been reported in the
paper.
Abstract: Frequent patterns are patterns such as sets of features or items that appear in data frequently. Finding such frequent patterns has become an important data mining task because it reveals associations, correlations, and many other interesting relationships hidden in a dataset. Most of the proposed frequent pattern mining algorithms have been implemented with imperative programming languages such as C, Cµ, Java. The imperative paradigm is significantly inefficient when itemset is large and the frequent pattern is long. We suggest a high-level declarative style of programming using a functional language. Our supposition is that the problem of frequent pattern discovery can be efficiently and concisely implemented via a functional paradigm since pattern matching is a fundamental feature supported by most functional languages. Our frequent pattern mining implementation using the Haskell language confirms our hypothesis about conciseness of the program. The performance studies on speed and memory usage support our intuition on efficiency of functional language.
Abstract: Data stream analysis is the process of computing
various summaries and derived values from large amounts of data
which are continuously generated at a rapid rate. The nature of a
stream does not allow a revisit on each data element. Furthermore,
data processing must be fast to produce timely analysis results. These
requirements impose constraints on the design of the algorithms to
balance correctness against timely responses. Several techniques
have been proposed over the past few years to address these
challenges. These techniques can be categorized as either dataoriented
or task-oriented. The data-oriented approach analyzes a
subset of data or a smaller transformed representation, whereas taskoriented
scheme solves the problem directly via approximation
techniques. We propose a hybrid approach to tackle the data stream
analysis problem. The data stream has been both statistically
transformed to a smaller size and computationally approximated its
characteristics. We adopt a Monte Carlo method in the approximation
step. The data reduction has been performed horizontally and
vertically through our EMR sampling method. The proposed method
is analyzed by a series of experiments. We apply our algorithm on
clustering and classification tasks to evaluate the utility of our
approach.
Abstract: Data mining (DM) is the process of finding and extracting frequent patterns that can describe the data, or predict unknown or future values. These goals are achieved by using various learning algorithms. Each algorithm may produce a mining result completely different from the others. Some algorithms may find millions of patterns. It is thus the difficult job for data analysts to select appropriate models and interpret the discovered knowledge. In this paper, we describe a framework of an intelligent and complete data mining system called SUT-Miner. Our system is comprised of a full complement of major DM algorithms, pre-DM and post-DM functionalities. It is the post-DM packages that ease the DM deployment for business intelligence applications.
Abstract: The state of the art in instructional design for
computer-assisted learning has been strongly influenced by advances
in information technology, Internet and Web-based systems. The
emphasis of educational systems has shifted from training to
learning. The course delivered has also been changed from large
inflexible content to sequential small chunks of learning objects. The
concepts of learning objects together with the advanced technologies
of Web and communications support the reusability, interoperability,
and accessibility design criteria currently exploited by most learning
systems. These concepts enable just-in-time learning. We propose to
extend theses design criteria further to include the learnability
concept that will help adapting content to the needs of learners. The
learnability concept offers a better personalization leading to the
creation and delivery of course content more appropriate to
performance and interest of each learner. In this paper we present a
new framework of learning environments containing knowledge
discovery as a tool to automatically learn patterns of learning
behavior from learners' profiles and history.