Walid Atwa - Academia.edu (original) (raw)
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D.G.Vaishnav College,Chennai 106
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Papers by Walid Atwa
International Journal of Intelligent Systems and Applications
Outlier detection is one of the important tasks in data mining. Detecting outliers over streaming... more Outlier detection is one of the important tasks in data mining. Detecting outliers over streaming data has become an important task in many applications, such as network analysis, fraud detections, and environment monitoring. One of the well-known outlier detection algorithms called Local Outlier Factor (LOF). However, the original LOF has many drawbacks that can’t be used with data streams: 1- it needs a lot of processing power (CPU) and large memory to detect the outliers. 2- it deals with static data which mean that in any change in data the LOF recalculates the outliers from the beginning on the whole data. These drawbacks make big challenges for existing outlier detection algorithms in terms of their accuracies when they are implemented in the streaming environment. In this paper, we propose a new algorithm called GSILOF that focuses on detecting outliers from data streams using genetics. GSILOF solve the problem of large memory needed as it has fixed memory bound. GSILOF has t...
International Journal of Information Technology and Computer Science
Semi.-supervised clustering algorithms aim to enhance the performance of clustering using the pai... more Semi.-supervised clustering algorithms aim to enhance the performance of clustering using the pairwise constraints. However, selecting these constraints randomly or improperly can minimize the performance of clustering in certain situations and with different applications. In this paper, we select the most informative constraints to improve semi-supervised clustering algorithms. We present an active selection of constraints, including active must.-link (AML) and active cannot.-link (ACL) constraints. Based on Radial-Bases Function, we compute lower-bound and upper-bound between data points to select the constraints that improve the performance. We test the proposed algorithm with the base-line methods and show that our proposed active pairwise constraints outperform other algorithms.
Research Journal of Applied Sciences, Engineering and Technology
Lecture Notes in Computer Science, 2014
Lecture Notes in Computer Science, 2015
International Journal of Intelligent Systems and Applications
Outlier detection is one of the important tasks in data mining. Detecting outliers over streaming... more Outlier detection is one of the important tasks in data mining. Detecting outliers over streaming data has become an important task in many applications, such as network analysis, fraud detections, and environment monitoring. One of the well-known outlier detection algorithms called Local Outlier Factor (LOF). However, the original LOF has many drawbacks that can’t be used with data streams: 1- it needs a lot of processing power (CPU) and large memory to detect the outliers. 2- it deals with static data which mean that in any change in data the LOF recalculates the outliers from the beginning on the whole data. These drawbacks make big challenges for existing outlier detection algorithms in terms of their accuracies when they are implemented in the streaming environment. In this paper, we propose a new algorithm called GSILOF that focuses on detecting outliers from data streams using genetics. GSILOF solve the problem of large memory needed as it has fixed memory bound. GSILOF has t...
International Journal of Information Technology and Computer Science
Semi.-supervised clustering algorithms aim to enhance the performance of clustering using the pai... more Semi.-supervised clustering algorithms aim to enhance the performance of clustering using the pairwise constraints. However, selecting these constraints randomly or improperly can minimize the performance of clustering in certain situations and with different applications. In this paper, we select the most informative constraints to improve semi-supervised clustering algorithms. We present an active selection of constraints, including active must.-link (AML) and active cannot.-link (ACL) constraints. Based on Radial-Bases Function, we compute lower-bound and upper-bound between data points to select the constraints that improve the performance. We test the proposed algorithm with the base-line methods and show that our proposed active pairwise constraints outperform other algorithms.
Research Journal of Applied Sciences, Engineering and Technology
Lecture Notes in Computer Science, 2014
Lecture Notes in Computer Science, 2015