Publications
Academic Journals
2011
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- Mahmoud Reza Saybani, Teh Ying Wah, Amineh Amini, and Saeed Reza Aghabozorgi. "Anomaly Detection and Prediction of Sensors Faults in a Refinery using Data Mining Techniques and Fuzzy Logic", Journal of Scientific Research and Essays, Vol. 6(27), pp. 5685-5695, 16 November, 2011. (ISI/SCOPUS Cited Publication)
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DOI: 10.5897/SRE11.333
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Like all manufacturing companies, refineries use many sensors to monitor and control the process of refining, therefore it is very crucial to detect any sensor faults or anomalies as early as possible, and to be able to replace or repair a sensor well in advance of any fault. Objective of this paper is to present a method for detecting anomalies in a sensor data, as well as to predict next occurance of a sensor failure. Data mining techniques to detect anomaly in sensor data and predict the occurrence of next faulty event were introduced. For anomaly detection, this research used MATLAB’s fuzzy logic toolbox tools to find clusters which uses subtractive fuzzy clustering algorithm and generates a model, a Sugeno-type fuzzy inference system. The same toolbox was used to evaluate the model with a promising result. To predict sensor fault, the original time series were used to create a new ‘derived time series’. Two prediction models known as ‘auto regressive integrated moving average’ and ‘autoregressive tree models’ were used against the new time series to predict next occurrence of sensor failure. The results oft hese models were compared. The model developed and introduced in this paper serves as an additional tool, which helps not only engineers and operators of oil refineries, but also other engineers of other disciplines to work more efficiently.
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- Mahmoud Reza Saybani, Teh Ying Wah, Amineh Amini, Saeed Reza Aghabozorgi, and Adel Lahsasna. "Applications of Support Vector Machines in Oil Refineries: A Survey", International Journal of the Physical Sciences, Vol. 6(27), pp. 6295-6302, 2 November, 2011. (ISI/SCOPUS Cited Publication)
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DOI: 10.5897/IJPS11.104
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Support vector machine has been explored and many applications found within various research areas and application domains. Many support vector machine techniques have been specifically developed for certain application domains. This paper is an attempt to provide an overview on applications of support vector machines within the oil refineries to the professionals inside oil refineries, researchers and academicians. This paper has grouped and summarized applications of support vector machines within various units inside refineries. Application of support vector machines to a particular domain within refineries can be used as guidelines to assess the effectiveness of the support vector machines in that domain. This survey provides a better understanding of the different applications that have been developed for one area which allows finding of applications in other domains.
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- Saeed Reza Aghabozorgi, Mahmoud Reza Saybani and Teh Ying Wah. "Incremental Clustering of Time Series Data by Fuzzy Clustering", Journal of Information Science and Engineering. (ISI/SCOPUS Cited Publication)
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Today, analyzing the user's behavior has gained wide importance in the data mining community. Typically, the behavior of a user is defined as a time series of his or her activities. In this paper, users are clustered based upon time series extracted from their behavior during the interaction with given system. Although there are several different techniques used to cluster time series and sequences, this paper will attack the problem by utilizing a novel incremental fuzzy clustering strategy in order to achieve the objective. Upon dimensionality reduction, time series data are pre-clustered using the longest common subsequence as an indicator for similarity measurement. Afterwards, by utilizing an efficient method, clusters are updated incrementally and periodically through a set of fuzzy approaches. In addition, we will present the benefits of the proposed system by implementing a real application: Customer Segmentation. In addition to having a low complexity, this approach can provide a deeper and more unique perspective for clustering of time series.
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Proceedings
2011
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- Mahmoud Reza Saybani, Teh Ying Wah, Adel Lahsasna, Amineh Amini, and Saeed Reza Aghabozorgi. "Data Mining Techniques for Predicting Values of a Faulty Sensor at a Refinery", in Proceedings of The 6th International Conference on Computer Sciences and Convergence Information Technology, Jeju Island, South Korea, November 29 - December 1, 2011, pp. XX-XX.
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DOI:
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Refineries rely heavily on the sensor data, decisions making in critical situation when a sensor failure happens is therefore essential. This paper proposes a method of predicting sensor values based on its historical data captured as time series. Main forecasting techniques such as linear regression, moving average, autoregressive integrated moving average; and exponential smoothing were used to predict the value of failed sensors. A comparison of the models based on their mean squared error is presented in order to simplify the selection of forecasting models. The proposed model assists engineers and experts at a refinery to make critical decision at critical moments.
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- Amineh Amini, Teh Ying Wah, Mahmoud Reza Saybani, and Saeed Reza Aghabozorgi "A Study of Density-Grid based Clustering Algorithms on Data Streams", in Proceedings of The 8th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), Shanghai, China, July 2011, pp. 1652-1656.
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DOI: 10.1109/FSKD.2011.6019867
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Clustering data streams attracted many researchers since the applications that generate data streams have become more popular. Several clustering algorithms have been introduced for data streams based on distance which are incompetent to find clusters of arbitrary shapes and cannot handle the outliers. Density-based clustering algorithms are remarkable not only to find arbitrarily shaped clusters but also to deal with noise in data. In density-based clustering algorithms, dense areas of objects in the data space are considered as clusters which are segregated by low-density area. Another group of the clustering methods for data streams is grid-based clustering where the data space is quantized into finite number of cells which form the grid structure and perform clustering on the grids. Grid-based clustering maps the infinite number of data records in data streams to finite numbers of grids. In this paper we review the grid based clustering algorithms that use density-based algorithms or density concept for the clustering. We called them density-grid clustering algorithms. We explore the algorithms in details and the merits and limitations of them. The algorithms are also summarized in a table based on the important features. Besides that, we discuss about how well the algorithms address the challenging issues in the clustering data streams.
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- Saeed Reza Aghabozorgi, Teh Ying Wah, Amineh Amini, and Mahmoud Reza Saybani "A New Approach to Present Prototypes in Clustering of Time Series", in Proceedings of The 7th International Conference of Data Mining, Las Vegas, USA, July 2011, pp. 214-220.
PDF: Download Proceedings
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There are considerable advances in clustering time series data in data mining concept. However, most of which use traditional approaches and try to customize the algorithms to be compatible with time series data. One of the significant problems with traditional clustering is defining prototype specially in partitional clustering where it needs centroids as representative of each cluster. In this paper we present a novel effective approach to define the prototypes based on time series nature. The prototype is constructed based on fuzzy concept efficiently. Moreover, it is demonstrated how the prototypes are moved in iterations. We will present the benefits of the proposed prototype by implementing a real application: Customer transactions clustering.
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- Amineh Amini, and Teh Ying Wah, "Density Micro-Clustering Algorithms on Data Streams: A Review", in Proceedings of the International Conference on Data Mining and Applications (ICDMA'11) in the International MultiConference of Engineers and Computer Scientists (IMECS'2011), Hong Kong, March 2011, pp. 410-414
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Data streams are massive, fast-changing, and infinite. Applications of data streams can vary from critical scientific and astronomical applications to important business and financial ones. They need algorithms to make a single pass with limited time and memory. Mining data streams is concerned with extracting knowledge structures represented in models and patterns in non-stopping data streams. Clustering is a prominent task in mining data streams, which group similar objects in a cluster. Several clustering algorithms have been introduced in recent years for data streams that are based on distance, so they can find only spherical shapes. Therefore, density-based clustering algorithms are adopted for data streams with ability for not only discovering the arbitrary shape clusters, but also for providing protection against the outliers. In fact, in density-based clustering algorithms, dense areas of objects in the data space are considered as clusters, which are segregated by low density area (noise). However, in the clustering data streams, due to certain characteristics, it is impossible to record all the data. Micro-clusters are a technique in stream clustering that maintains the compact information about the data objects in data streams. Micro-cluster is a temporal extension of the cluster feature, which compresses the data effectively. In this paper, we intend to review the outstanding density-based clustering algorithms on data streams using micro-clusters. We will explore algorithm characteristics and analyze their merits and limitations.
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2010
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- Mahmoud Reza Saybani, and Teh Ying Wah, "Data mining and data gathering in a refinery", in Proceedings of the 10th WSEAS International Conference on Applied Computer Science (ACS), Iwate, Japan, October 2010, pp. 62-66
PDF: Download
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This article handles one of critical steps of data mining, which is data collection. It will show how the researcher could get access to the valuable data of a refinery. And it explains the procedures of refining criteria for data collection. It also briefly explains the oil refining procedures to make the concept of data gathering at the refinery easier to understand. Each manufacturing company has its own specifications and rules that are needed to be considered when collecting data. As such the result of data gathering is almost always different for different manufacturing companies.
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2009
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- Saeed Reza Aghabozorgi, and Teh Ying Wah, "Using Incremental Fuzzy Clustering to Web Usage Mining", in Proceedings of International Conference of Soft Computing and Pattern Recognition (SoCPaR), Malacca, Malaysia, December 2009, pp. 653-658
DOI: 10.1109/SoCPaR.2009.128
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The recent extensive growth of data on the Web, has generated an enormous amount of log records on Web server databases. Applying Web usage mining techniques on these vast amounts of historical data can discover potentially useful patterns and reveal user access behaviors on the Web site. Cluster analysis has widely been applied to generate user behavior models on server Web logs. Most of these off-line models have the problem of the decrease of accuracy over time resulted of new users joining or changes of behavior for existing users in model-based approaches. This paper proposes a novel approach to generate dynamic model from off-line model created by fuzzy clustering. In this method, we will use users' transactions periodically to change the off-line model. To this aim, an improved model of leader clustering along with a static approach is used to regenerate clusters in an incremental fashion.
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- Mahmoud Reza Saybani, Teh Ying Wah, and Adel Lahsasna, "Applied Data Mining Approach in Ubiquitous World of Air Transportation", in Proceedings of Fourth International Conference on Computer Sciences and Convergence Information Technology, Seoul, Korea, November 2009, pp. 1218-1222
DOI: 10.1109/ICCIT.2009.255
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Noise is a big problem for people living near airports, therefore the public, airport authorities and pilots are looking for ways to reduce the noise in the vicinity of populated areas. Optimal solution would be flight paths that are farthest from those areas, and worst paths are those, that just go above them. There are two classes of paths, namely optimal and non-optimal ones. This paper is going to use one of successfully used data mining techniques, namely neural network, which is capable of recognizing patterns. We used some coordinates of various flight paths as input for learning purposes of Neural Network, and defined two classes representing the optimal and non-optimal flight paths. The results have shown that this technique is well capable of recognizing the optimal and non-optimal flight paths. This technique can be used to reduce the noise.
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- Saeed Reza Aghabozorgi, and Teh Ying Wah, "Recommender systems: incremental clustering on web log data", in Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human (ICIS), Seoul, Korea, November 2009, pp. 812-818
DOI: 10.1145/1655925.1656073
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Nowadays, recommendation systems are definitely a necessity in the websites not just an auxiliary feature, especially for commercial websites and web sites with large information services. Recommendation systems use models constructed by applying statistical and data mining approaches on derived data from websites. In this paper we propose a new hybrid approach that leverages usage data and data domain of website to construct a recommendation model. A data mining model will be created by applying clustering algorithm, and then the model is adjusted by statistical approach based on the change of behavior of users or data domain of website periodically. We believe that by this novel approach the problem of inaccuracy of conventional usage data models partly due to slowly change of behavior of users or data domain of websites will be solved.
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- Saeed Reza Aghabozorgi, and Teh Ying Wah, "Dynamic Modeling by Usage Data for Personalization Systems", in Proceedings of 13th International Conference of Information Visualisation, Barcelona, Spain, July 2009, pp. 450-455
DOI: 10.1109/IV.2009.111
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With the extensive growth of data available on the Internet, personalization of this huge information becomes essential. Although, there are various techniques of personalization, in this paper we concentrate on using data mining algorithms to personalize web sitespsila usage data. This paper proposes an off-line model based web usage mining that is generated by clustering algorithm.Then, we will use userspsila transactions periodically to change the off-line model to a dynamic-model.This proposed approach will solve the problem of the decrease of accuracy in the off-line models over time resulted of new users joining or changes of behaviour for existing users in model-based approaches. Finally, we discuss the on-line model for user behaviour prediction in the web personalization system.
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Meetings
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Date |
Speaker |
Attendees |
Title |
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| January 17, 2012 - Fenruary 7, 2012 |
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AA,SA,MS |
Book: Data Mining Practical Machine Learning Tools and Techniques |
| December 13, 2011 |
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AA,SA,MS |
General Discussion |
| December 6, 2011 |
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AA,SA,MS |
Cluster Evaluation |
| June 3, 2011 |
Amineh Amini |
AA,SA,MS |
Evaluation ... |
| June 3, 2011 |
Saeed Reza Aghabozorgi |
AA,SA,MS |
Evaluation ... |
| June 2-3, 2011 |
Hadi Saboohi |
AA,SA,MS |
Java for Weka |
| May 31-June 2, 2011 |
Dr. Diljit Singh |
SA,MS |
Workshop: Scholarly Publishing |
| May 24-25, 2011 |
Dr. Diljit Singh |
AA |
Workshop: Scholarly Publishing |
| May 23-24, 2011 |
Dr. Sapiyan Baba |
AA,MS |
Workshop: Specific Research Method Seminar |
| May 18-20, 2011 |
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AA,MS |
Workshop: Matlab |
| May 9-10, 2011 |
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MS |
Workshop: Statistic Analysis Workshop |
| March 13, 2011 |
Mahmoud Reza Saybani |
AA,SA,MS |
PASW Modeler: - |
| March 13, 2011 |
Saeed Reza Aghabozorgi |
AA,SA,MS |
PASW Modeler: - |
| March 13, 2011 |
Amineh Amini |
AA,SA,MS |
PASW Modeler: - |
| March 11, 2011 |
Mahmoud Reza Saybani |
AA,SA,MS |
PASW Modeler: Decision List |
| March 9, 2011 |
Mahmoud Reza Saybani |
AA,SA,MS |
Correlation, Coefficient, PASW Modeler: Decision List |
| March 4, 2011 |
Saeed Reza Aghabozorgi |
AA,SA,MS |
PASW Modeler: Auto Cluster, Quest, Linear |
| February 23, 2011 |
Amineh Amini |
TYW,AA,SA,MS |
PASW Modeler: Time Series, Quest |
| February 23, 2011 |
Mahmoud Reza Saybani |
TYW,AA,SA,MS |
PASW Modeler: C&R Tree |
| February 16, 2011 |
Amineh Amini |
AA,SA,MS |
PASW Modeler: Auto Classifier |
| February 16, 2011 |
Mahmoud Reza Saybani |
AA,SA,MS |
PASW Modeler: Auto Numeric |
| January 26, 2011 |
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AA,SA,MS |
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| January 19, 2011 |
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AA,SA,MS |
First Meeting, Opening |
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