A novel defect prediction method for web pages using k-means++

Öztürk, Muhammed Maruf; Çavuşoğlu, Ünal; Zengi̇n, Ahmet

doi:10.1016/j.eswa.2015.03.013

Cited by 34 publications

(18 citation statements)

References 33 publications

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“…Phan et al [8], the hierarchical clustering algorithm is combined with the famous independent waterfall model IC to analyze the user's sports behavior characteristics and construct a physical exercise communication model TaCPP to obtain the relationship between users and physical exercise. In [9]- [11], the user's browsing behavior is analyzed to predict the webpage access. Wang et al [9] mainly uses the clickstream data to capture the user's behavior, and uses the similarity between the clickstreams to construct the similarity graph.…”

Section: A Related Workmentioning

confidence: 99%

User Behavior Clustering Scheme With Automatic Tagging Over Encrypted Data

Gao

Wang

et al. 2019

IEEE Access

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User behavior clustering analysis has a wide range of applications in business intelligence, information retrieval, and image pattern recognition and fault diagnosis. Most of existing methods of user behavior have some problems such as weak generality and the lack of tags of clustering. With the increasing awareness of privacy protection, user behavior analysis also needs to support for ciphertext to protect user data. Based on clustering algorithm, homomorphic encryption technology and information security, in this paper, we propose a user behavior clustering scheme that supports automatic tags on ciphertext. Firstly, design a security protocol corresponding to the basic operations such as addition, multiplication and comparison and apply to the scheme. Then, the relevant features of the user behavior are merged with the clustering process, the latent factor model, and matrix decomposition. We have implemented our method and evaluated its performance using K-means and K-means++ clustering. The results show that the scheme can auto tags over encrypted data, and the tag also meets the actual situation, which proves the validity and generality of the scheme.

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Section: A Related Workmentioning

confidence: 99%

User Behavior Clustering Scheme With Automatic Tagging Over Encrypted Data

Gao

Wang

et al. 2019

IEEE Access

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“…Kmeans terimi ilk defa James MacQueen tarafından 1967'de kullanılmıştır [22,23]. K-means basitliği sebebi ile veri madenciliği başta olmak üzere çok çeşitrli alanlarda uygulamaya sahiptir [24].…”

Section: K-means Kümelemeunclassified

The classification of breast cancer with Machine Learning Techniques

Kolay

Erdoğmuş

2016

2016 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT)

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ÖzetçeBu çalışmada UCI(University of California-Irvine) Machine Learning Laboratuvarından alınan göğüs kanseri dataları kullanılarak, makine öğrenmesi teknikleri ile göğüs kanserinin sınıflandırılmasına çalışılmıştır. Bu amaçla, göğüs kanseri dataları kullanılarak, K-means kümeleme uzaklık ölçümlerinin kümeleme başarısı Matlab©'da analiz edilmiştir. Daha sonra, hiçbir ön işleme yapmadan, Weka© Veri Madenciliği yazılımı kullanılarak, UCI'den alınan göğüs kanseri dataları, çeşitli makine öğrenme teknikleri ile WEKA'da sınıflandırılmıştır. Sonuç olarak kümeleme uzaklık ölçütlerinin başarıyı yaklaşık olarak %12 değiştirdiği ve sınıflandırma başarısının yöntemlere göre %45-%79 arasında değiştiği görülmüştür. AbstractIn this study, it is aimed to classify breast cancer data attained from UCI(University of California-Irvine), Machine Learning Laboratory with some Machine Learning Techniques. With this aim, clustering performance of some distance measures in Matlab© has been compared, using breast cancer data. Later without using any pre-processing, some of the machine learning techniques are used for the clustering breast cancer data, using WEKA data mining software©. As a result, it has been seen that distance measures effects the clustering performance nearly 12 percentage and the succes of the classification varies from %45 to %79, according to the methods.

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“…teams locate the defective code more easily [1]- [3]. Software defect prediction has attracted many researchers in recent years [4]- [7]. However, the prerequisite to perform SDP is to obtain sufficient historical data (i.e.…”

Section: Introductionmentioning

confidence: 99%

A Suitable AST Node Granularity and Multi-Kernel Transfer Convolutional Neural Network for Cross-Project Defect Prediction

Deng

Qiu

et al. 2020

IEEE Access

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Cross-project defect prediction (CPDP) is a feasible way to perform software defect prediction (SDP) when lacking historical data. Recent CPDP approaches have employed deep learning techniques to better exploit the information from the program's abstract syntax trees (ASTs). However, the granularity of the AST nodes and the data distribution difference between projects may have negative impacts on the prediction performance, which many CPDP studies didn't take into consideration. To handle these issues, this paper explores a better AST node granularity and proposes a CPDP framework based on multi-kernel transfer convolutional neural networks. Specifically, for AST node granularity, we explore the difference of three AST node granularities and then compare the prediction performance of each granularity on several prediction models. For the CPDP framework, we first parse the program source code into ASTs and then encode the AST nodes into numerical vectors using the embedding technique. Secondly, to mine transferable semantic features, the encoded ASTs are fed into a convolutional neural network, in which a multi-kernel matching layer is added to minimize the data distribution divergence between the source and target project. Finally, to make use of the information from the handcrafted features, the semantic features mined from the AST are joint with handcrafted features to form the joint features for CPDP. We evaluate our approach on 110 CPDP tasks formed by 11 open-source projects and results show that the proposed CPDP method outperforms most deep learning-based approaches. INDEX TERMS Abstract syntax tree, cross-project defect prediction, maximum mean discrepancy, multikernel, transfer learning.

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A novel defect prediction method for web pages using k-means++

Cited by 34 publications

References 33 publications

User Behavior Clustering Scheme With Automatic Tagging Over Encrypted Data

User Behavior Clustering Scheme With Automatic Tagging Over Encrypted Data

The classification of breast cancer with Machine Learning Techniques

A Suitable AST Node Granularity and Multi-Kernel Transfer Convolutional Neural Network for Cross-Project Defect Prediction

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