Scope & Topics Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data.

1. April 2020: Top Read Articles In
Data Mining & Knowledge
Management Process Research
Articles
International Journal of Data Mining & Knowledge
Management Process (IJDKP)
ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print]
http://airccse.org/journal/ijdkp/ijdkp.html

2. A WEB REPOSITORY SYSTEM FOR DATA MINING IN
DRUG DISCOVERY
Jiali Tang, Jack Wang and Ahmad Reza Hadaegh
Department of Computer Science and Information System, California State University
San Marcos, San Marcos, USA
ABSTRACT
This project is to produce a repository database system of drugs, drug features
(properties), and drug targets where data can be mined and analyzed. Drug targets are
different proteins that drugs try to bind to stop the activities of the protein. Users can
utilize the database to mine useful data to predict the specific chemical properties that will
have the relative efficacy of a specific target and the coefficient for each chemical
property. This database system can be equipped with different data mining
approaches/algorithms such as linear, non-linear, and classification types of data
modelling. The data models have enhanced with the Genetic Evolution (GE) algorithms.
This paper discusses implementation with the linear data models such as Multiple Linear
Regression (MLR), Partial Least Square Regression (PLSR), and Support Vector Machine
(SVM).
.
KEYWORDS
Data Mining, Drug Discovery, Drug Description, Chemoinformatics, and Web
Application
For More Details : https://aircconline.com/ijdkp/V10N1/10120ijdkp01.pdf
Volume Link : http://airccse.org/journal/ijdkp/vol10.html

3. REFERENCES
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5. A REVIEW ON EVALUATION METRICS FOR DATA
CLASSIFICATION EVALUATIONS
Hossin, M.1
and Sulaiman, M.N.2
1
Faculty of Computer Science & Information Technology, Universiti Malaysia
Sarawak,
94300 Kota Samarahan, Sarawak, Malaysia
2
Faculty of Computer Science & Information Technology, Universiti Putra Malaysia,
43400 UPM Serdang, Selangor, Malaysia
ABSTRACT
Evaluation metric plays a critical role in achieving the optimal classifier during the
classification training. Thus, a selection of suitable evaluation metric is an important key
for discriminating and obtaining the optimal classifier. This paper systematically reviewed
the related evaluation metrics that are specifically designed as a discriminator for
optimizing generative classifier. Generally, many generative classifiers employ accuracy
as a measure to discriminate the optimal solution during the classification training.
However, the accuracy has several weaknesses which are less distinctiveness, less
discriminability, less in formativeness and bias to majority class data. This paper also
briefly discusses other metrics that are specifically designed for discriminating the optimal
solution. The shortcomings of these alternative metrics are also discussed. Finally, this
paper suggests five important aspects that must be taken into consideration in constructing
a new discriminator metric..
KEYWORDS
Evaluation Metric, Accuracy, Optimized Classifier, Data Classification Evaluation
For More Details : http://aircconline.com/ijdkp/V5N2/5215ijdkp01.pdf
Volume Link : http://airccse.org/journal/ijdkp/vol5.html

6. REFERENCES
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9. A BUSINESS INTELLIGENCE PLATFORM
IMPLEMENTED IN A BIG DATA SYSTEM
EMBEDDING DATA MINING: A CASE OF STUDY
Alessandro Massaro, Valeria Vitti, Palo Lisco, Angelo Galiano and Nicola Savino
Dyrecta Lab, IT Research Laboratory, Via VescovoSimplicio, 45, 70014 Conversano
(BA), Italy.
(in collaboration with ACI Global S.p.A., VialeSarca, 336 - 20126 Milano, Via Stanislao
Cannizzaro, 83/a - 00156 Roma, Italy)
ABSTRACT
In this work is discussed a case study of a business intelligence –BI- platform developed
within the framework of an industry project by following research and development –
R&D- guidelines of ‘Frascati’. The proposed results are a part of the output of different
jointed projects enabling the BI of the industry ACI Global working mainly in roadside
assistance services. The main project goal is to upgrade the information system, the
knowledge base –KB- and industry processes activating data mining algorithms and big
data systems able to provide gain of knowledge. The proposed work concerns the
development of the highly performing Cassandra big data system collecting data of two
industry location. Data are processed by data mining algorithms in order to formulate a
decision making system oriented on call center human resources optimization and on
customer service improvement. Correlation Matrix, Decision Tree and Random Forest
Decision Tree algorithms have been applied for the testing of the prototype system by
finding a good accuracy of the output solutions. The Rapid Miner tool has been adopted
for the data processing. The work describes all the system architectures adopted for the
design and for the testing phases, providing information about Cassandra performance and
showing some results of data mining processes matching with industry BI strategies..
KEYWORDS
Big Data Systems, Cassandra Big Data, Data Mining, Correlation Matrix, Decision Tree,
Frascati Guideline
Full Text :http://aircconline.com/ijdkp/V9N1/9119ijdkp01.pdf
.

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AUTHOR
Alessandro Massaro: Research & Development Chief of Dyrecta Lab s.r.l.

13. INCREASED PREDICTION ACCURACY IN THE GAME
OF CRICKETUSING MACHINE LEARNING
Kalpdrum Passi and Niravkumar Pandey
Department of Mathematics and Computer Science
Laurentian University, Sudbury, Canada
ABSTRACT
Player selection is one the most important tasks for any sport and cricket is no exception.
The performance of the players depends on various factors such as the opposition team,
the venue, his current form etc. The team management, the coach and the captain select 11
players for each match from a squad of 15 to 20 players. They analyze different
characteristics and the statistics of the players to select the best playing 11 for each match.
Each batsman contributes by scoring maximum runs possible and each bowler contributes
by taking maximum wickets and conceding minimum runs. This paper attempts to predict
the performance of players as how many runs will each batsman score and how many
wickets will each bowler take for both the teams. Both the problems are targeted as
classification problems where number of runs and number ofwickets are classified in
different ranges. We used naïve bayes, random forest, multiclass SVM and decision tree
classifiers to generate the prediction models for both the problems. Random Forest
classifier wasfound to be the most accurate for both the problems.
KEYWORDS
Naïve Bayes, Random Forest, Multiclass SVM, Decision Trees, Cricket
Full Text : http://aircconline.com/ijdkp/V8N2/8218ijdkp03.pdf
.

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AUTHORS
Kalpdrum Passi received his Ph.D. in Parallel Numerical Algorithms from Indian
Institute of Technology, Delhi, India in 1993. He is an Associate Professor,
Department of Mathematics & Co mputer Science, at Laurentian University, Ontario,
Canada. He has published many papers on Parallel Numerical Algorithms in
international journals and conferences. He has collaborative work with faculty in
Canada and US and the work was tested on the CRAY XMP’s and CRAY YMP’s. He
transitioned his research to web technology, and more recently has been involved in machine
learning and data mining applications in bioinformatics, social media and other data science areas.
He obtained funding from NSERC and Laurentian University for his research. He is a member of
the ACM and IEEE Computer Society.
Niravkumar Pandey is pursuing M.Sc. in Computational Science at Laurentian
University, Ontario, Canada. He received his Bachelor of Engineering degree from
Gujarat Technological University, Gujarat, India. Data mining and machine learning are
his primary areas of interest. He is also a cricket enthusiast and is studying applicationsof
machine learning and data mining in cricket analytics for his M.Sc. thesis

16. Data Mining and Its Applications for Knowledge Management :
A Literature Review from 2007 to 2012
Tipawan Silwattananusarn1and Assoc.Prof.Dr. KulthidaTuamsuk2
1Ph.D. Student in Information Studies Program,
KhonKaen University, Thailand
2Head, Information & Communication Management Program,
KhonKaen University, Thailand
ABSTRACT
Data mining is one of the most important steps of the knowledge discovery in databases
process and is considered as significant subfield in knowledge management. Research in
data mining continues growing in business and in learning organization over coming
decades. This review paper explores the applications of data mining techniques which
have been developed to support knowledge management process. The journal articles
indexed in ScienceDirect Database from 2007 to 2012 are analyzed and classified. The
discussion on the findings is divided into 4 topics: (i) knowledge resource; (ii) knowledge
types and/or knowledge datasets; (iii) data mining tasks; and (iv) data mining techniques
and applications used in knowledge management. The article first briefly describes the
definition of data mining and data mining functionality. Then the knowledge management
rationale and major knowledge management tools integrated in knowledge management
cycle are described. Finally, the applications of data mining techniques in the process of
knowledge management are summarized and discussed..
KEYWORDS
Data mining; Data mining applications; Knowledge management.
For More Details : http://aircconline.com/ijdkp/V2N5/2512ijdkp02.pdf
Volume Link :http://airccse.org/journal/ijdkp/vol2.htmll

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19. APPLICATION OF SPATIOTEMPORAL ASSOCIATION
RULES ON SOLAR DATA TO SUPPORT SPACE
WEATHER FORECASTING
Carlos Roberto Silveira Junior1
, José Roberto Cecatto2
, Marilde Terezinha Prado
Santos1
and Marcela Xavier Ribeiro1
1
Department of Computing, Federal University of São Carlos, São Carlos, Brazil
2
National Institute of Space Research, São José dos Campos, Brazil
ABSTRACT
It is well known that solar energetic phenomena influence the Space Weather, in special
those directed to the Earth environment. In this context, the analysis of Solar Data is a
challenging task, particularly when are composed of Satellite Image Time Series (SITS). It
is a multidisciplinary domain that generates a massive amount of data (several Gigabytes
per year). It includes image processing, spatiotemporal characteristics, and the processing
of semantic data. Aiming to enhance the SITS analysis, we propose an algorithm called
"Miner of Thematic Spatiotemporal Associations for Images" (MiTSAI), which is anb
extractor of Thematic Spatiotemporal Association Rules (TSARs) from Solar SITS. Here,
a description is given about the details of the modern algorithm MiTSAI, which is an
extractor of Thematic Spatiotemporal Association Rules (TSARs) from solar Satellite
Image Time Series (SITS). In addition, its adaptation to the Space Weather and discussion
about the specific use in favor of forecasting activities are presented. Finally, some results
of its application specifically to solar flare forecasting are also presented. MiTSAI has to
extract interesting new patterns compared with the art-state algorithms.
KEYWORDS
Data Mining, Drug Discovery, Drug Description, Chemoinformatics, and Web
Application
For More Details : https://aircconline.com/ijdkp/V10N2/10220ijdkp01.pdf
Volume Link : http://airccse.org/journal/ijdkp/vol10.html

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AUTHORS
Carlos Roberto Silveira Junior is a System Architect at Ericsson expert in software development for
telecom systems. He is a Ph.D. Student at Federal University of São Carlos (UFSCar) working with Data
Mining -Artificial Intelligence- with images processing and parallelism. Holds a Master Degree in Data
Mining and ontology at UFSCar, graduated Computer Science at UFSCar. Main areas of interest: data
mining, programming, and distributed system.
José Roberto Cecatto holds a BS in Physics from the Pontifical Catholic University of São Paulo, a
Master’s and a PhD in Astrophysics from the National Institute for Space Research (INPE). He is currently a
researcher at INPE. He has experience in Astronomy, with an emphasis on Radio Astronomy and
instrumental development, acting mainly on the following themes: solar, radio, spectroscopy and
interferometry. Currently, working in Space Climate with activities and development of tools related to the
forecast of solar phenomena.
Marilde Terezinha Prado Santos is an associate professor at the Center for Exact Sciences and
Technology / Department of Computing at the Federal University of São Carlos. PhD in Science with
emphasis in Computational Physics from the University of São Paulo (2000). Master in Computer Science
from the Federal University of São Carlos (1994) and Bachelor in Computer Science from the Pontifical
Catholic University of Rio Grande do Sul (1991). Main areas of interest: engineering and applications of
crisp and fuzzy ontologies, information retrieval, data mining, data integration and semantic web.