The latest issue of International Journal of Data Warehousing and Mining (IJDWM)

 
The contents of the latest issue of:

 International Journal of Data Warehousing and Mining (IJDWM)
 Official Publication of the Information Resources Management Association
 Volume 3, Issue 3, July-September 2007
 Published: Quarterly in Print and Electronically
 ISSN: 1548-3924 EISSN: 1548-3932
 Published by IGI Publishing, Hershey, PA, USA
 www.igi-global.com/ijdwm

 Editor-in-Chief: David Taniar, Monash University, Australia

 Guest Editor:
 Ioannis Vlahavas, Guest Editor, IJDWM
 Marek Wojciechowski, Guest Editor, IJDWM

 Special Issue: 2nd Advances in Databases and Information Systems (ADBIS)
 Workshop on Data Mining and Knowledge Discovery (ADMKD)

 EDITORIAL PREFACE:

 "Special Issue: on 2nd Advances in Databases and Information Systems (ADBIS)
 Workshop on Data Mining and Knowledge Discovery (ADMKD) "

 Ioannis Vlahavas, Guest Editor, IJDWM
 Marek Wojciechowski, Guest Editor, IJDWM

 The second Advances in Databases and Information Systems (ADBIS)
Workshop on Data Mining and Knowledge Discovery (ADMKD) was held in
Thessaloniki, Greece, September 6, 2006, in conjunction with the 10th
East-European Conference on ADBIS. The ADBIS series of conferences has
provided since 1997 an international platform for the presentation of
research on database theory and development of advanced DBMS
technologies and their advanced applications. The workshop built on
the success of the first ADMKD workshop, which was started as a forum
to encourage researchers and practitioners to discuss and investigate
data mining research and implementation issues. It was a well-attended
workshop that presented sessions on highly relevant technical topics,
demonstrating the current state of the art in development and
deployment of new data mining algorithms and systems.

 To read the preface, please consult this issue of IJDWM in your library.

 PAPER ONE:

 "Multi-Label Classification: An Overview"

 Grigorios Tsoumakas, Aristotle University of Thessaloniki, Greece
 Ioannis Katakis, Aristotle University of Thessaloniki, Greece

 Multi-label classification methods are increasingly required by
modern applications, such as protein function classification, music
categorization, and semantic scene classification. This article
introduces the task of multi-label classification, organizes the
sparse related literature into a structured presentation and performs
comparative experimental results of certain multi-label classification
methods. It also contributes the definition of concepts for the
quantification of the multi-label nature of a data set.

 To obtain a copy of the entire article, click on the link below.
 www.igi-global.com/articles/details.asp?ID=7226

 PAPER TWO:

 "Robust Classification Based on Correlations Between Attributes"

 Alexandros Nanopoulos, Aristotle University of Thessaloniki, Greece
 Apostolos N. Papadopoulos, Aristotle University of Thessaloniki, Greece
 Yannis Manolopoulos, Aristotle University of Thessaloniki, Greece
 Tatjana Welzer-Druzovec, University of Maribor, Slovenia

 The existence of noise in the data signi cantly impacts the accuracy
of classi cation. In this article, we are concerned with the
development of novel classi cation algorithms that can e ciently
handle noise. To attain this, we recognize an analogy between k
nearest neighbors (kNN) classi cation and user-based collaborative
ltering algorithms, as they both nd a neighborhood of similar past
data and process its contents to make a prediction about new data. The
recent development of item-based collaborative ltering algorithms,
which are based on similarities between items instead of transactions,
addresses the sensitivity of user-based methods against noise in
recommender systems. For this reason, we focus on the item-based
paradigm, compared to kNN algorithms, to provide improved robustness
against noise for the problem of classi cation. We propose two new
item-based algorithms, which are experimentally evaluated with kNN.
Our results show that, in terms of precision, the proposed methods
outperform kNN classi cation by up to 15%, whereas compared to other
methods, like the C4.5 system, improvement exceeds 30%.

 To obtain a copy of the entire article, click on the link below.
 www.igi-global.com/articles/details.asp?ID=7227

 PAPER THREE:

 "A Single Pass Algorithm for Discovering Significant Intervals in
Time-Series Data"

 Sagar Savla, The University of Texas at Arlington, USA
 Sharma Chakravarthy, The University of Texas at Arlington, USA

 Sensor-based applications, such as smart homes, require prediction of
event occurrences for automating the environment using time-series
data collected over a period of time. In these applications, it is
important to predict events in tight and accurate intervals to
effectively automate the application. This article deals with the
discovery of significant intervals from time-series data. Although
there is a considerable body of work on sequential mining of
transactional data, most of them deal with time-point data and make
several passes over the entire data set in order to discover
frequently occurring patterns/events. We propose an approach in which
significant intervals representing intrinsic nature of data are
discovered in a single pass. In our approach, time-series data is
folded over a periodicity (day, week, etc.) in which the intervals are
formed. Significant intervals are discovered from this interval data
that satisfy the criteria of minimum confidence and maximum interval
length specified by the user. Both compression and working with
intervals contribute towards improving the efficiency of the
algorithm. In this article, we present a new single-pass algorithm for
detecting significant intervals; discuss its characteristics,
advantages, and disadvantages; and analyze it. Finally, we compare the
performance of our algorithm with previously developed level-wise and
SQL-based algorithms for significant interval discovery (SID).

 To obtain a copy of the entire article, click on the link below.
 www.igi-global.com/articles/details.asp?ID=7228

 PAPER FOUR:

 "Mining for Mutually Exclusive Items in Transaction Databases"

 George Tzanis, Aristotle University of Thessaloniki, Greece
 Christos Berberidis, Aristotle University of Thessaloniki, Greece

 Association rule mining is a popular task that involves the discovery
of co-occurences of items in transaction databases. Several extensions
of the traditional association rule mining model have been proposed so
far; however, the problem of mining for mutually exclusive items has
not been directly tackled yet. Such information could be useful in
various cases (e.g., when the expression of a gene excludes the
expression of another), or it can be used as a serious hint in order
to reveal inherent taxonomical information. In this article, we
address the problem of mining pairs of items, such that the presence
of one excludes the other. First, we provide a concise review of the
literature, then we define this problem, we propose a
probability-based evaluation metric, and finally a mining algorithm
that we test on transaction data.

 To obtain a copy of the entire article, click on the link below.
 www.igi-global.com/articles/details.asp?ID=7229

 PAPER FIVE:

 "Feature Selection for the Promoter Recognition and Prediction Problem"

 George Potamias, Institute of Computer Science, FORTH, Greece
 Alexandros Kanterakis, Institute of Computer Science, FORTH, Greece

 With the completion of various whole genomes, one of the fundamental
bioinformatics tasks is the identification of functional regulatory
regions, such as promoters, and the computational discovery of genes
from the produced DNA sequences. Confronted with huge amounts of DNA
sequences, the utilization of automated computational sequence
analysis methods and tools is more than demanding. In this article, we
present an efficient feature selection to the promoter recognition,
prediction, and localization problem. The whole approach is
implemented in a system called MineProm. The basic idea underlying our
approach is that each position-nucleotide pair in a DNA sequence is
represented by a distinct binary-valued feature­the binary position
base value (BPBV). A hybrid filter-wrapper, featuredeletion (or
addition) algorithmic process is called for in order to select those
BPBVs that best discriminate between two DNA sequences target classes
(i.e., promoter vs. nonpromoter). MineProm is tested on two widely
used benchmark data sets. Assessment of results demonstrates the
reliability of the approach.

 To obtain a copy of the entire article, click on the link below.
 www.igi-global.com/articles/details.asp?ID=7230

 *****************************************************
 For full copies of the above articles, check for this issue of the
International Journal of Data Warehousing and Mining (IJDWM) in your
institution's library. If your library is not currently subscribed to
this journal, please recommend an IJDWM subscription to your
librarian.
 *****************************************************

 CALL FOR PAPERS

 Mission of IJDWM:

 The International Journal of Data Warehousing and Mining (IJDWM)
publishes and disseminates knowledge on an international basis in the
areas of data warehousing and data mining. It is published multiple
times a year, with the purpose of providing a forum for
state-of-the-art developments and research, as well as current
innovative activities in data warehousing and mining. In contrast to
other journals, this journal focuses on the integration between the
fields of data warehousing and data mining, with emphasis on the
applicability to real world problems. The journal is targeted at both
academic researchers and practicing IT professionals.

 Coverage of IJDWM:

 Data mart and practical issues

 Data mining methods

 Data models

 Data structures

 Design data warehousing process

 Online analytical process

 Tools and languages

 The journal is devoted to the publications of high quality papers on
theoretical developments and practical applications in data
warehousing and data mining. Original research papers,
state-of-the-art reviews, and technical notes are invited for
publications.

 Interested authors should consult the journal's manuscript submission
guidelines at www.igi-global.com/ijdwm .

 All inquiries and submissions should be sent to:
 Editor-in-Chief: Dr. David Taniar at david.taniar@infotech.monash.edu.au
Received on Tue Aug 7 07:45:54 2007