The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS
Transactions ID Number: 27-651
Full Name: Alaaeldin Hafez
Position: Professor
Age: ON
Sex: Male
Address: Department of Information Systems, College of Computer and Information Sciences, King Saud University, P.O. Box 2454 Riyadh 11451
Country: SAUDI ARABIA
Tel: 529-5367
Tel prefix: +96650
Fax: +9661-467-6591
E-mail address: ahafez2001@yahoo.com
Other E-mails: hatim@ccis.ksu.edu.sa
Title of the Paper: an efficient stream mining technique
Authors as they appear in the Paper: Hatim A. Aboalsamh, Alaaeldin M. Hafez, Ghazy M. R. Assassa
Email addresses of all the authors: ahafez2001@yahoo.com, hatim@ccis.ksu.edu.sa, ghazy@ccis.ksu.edu.sa
Number of paper pages: 10
Abstract: Stream analysis is considered as a crucial component of strategic control over a broad variety of disciplines in business, science and engineering. Stream data is a sequence of observations collected over intervals of time. Each data stream describes a phenomenon. Analysis on Stream data includes discovering trends (or patterns) in a Stream sequence. In the last few years, data mining has emerged and been recognized as a new technology for data analysis. Data Mining is the process of discovering potentially valuable patterns, associations, trends, sequences and dependencies in data. Data mining techniques can discover information that many traditional business analysis and statistical techniques fail to deliver. In our study, we emphasis on the use of data mining techniques on data streams, where mining techniques and tools are used in an attempt to recognize, anticipate and learn the stream behavior with different directly related or looked unrelated factors.!
Targeted data are sequences of observations collected over intervals of time. Each sequence describes a phenomenon or a factor. Such factors could have either a direct or indirect impact on the stream data under study. Examples of factors with direct impact include the yearly budgets and expenditures, taxations, local stocks prices, unemployment rates, inflation rates, fallen angels, and rising odds for upgrades. Indirect factors could include any phenomena in the local or global environments, such as, global stocks prices, education expenditures, weather conditions, employment strategies, and medical services. Analysis on data includes discovering trends (or patterns) and association between sequences in order to generate non-trivial knowledge. In this paper, we propose a data mining technique to predict the dependency between factors that affect performance. The proposed technique consists of three phases: (a) for each data sequence that represents a chosen phenomenon, g!
enerate its trend sequences, (b) discover maximal frequent trend patte
rns, generate pattern vectors (to keep information of frequent trend patterns), use trend pattern vectors to predict future factor sequences.
Keywords: Data mining, Stream mining, Time series mining, Mining trends, Data sequences, Association mining, Maximal trend patterns, Global trends, Local trends.
EXTENSION of the file: .pdf
Special (Invited) Session: An Efficient Time Series Data Mining Technique
Organizer of the Session: 591-956
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