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Comparison of clustering algorithms on generalized propensity score in observational studies: a simulation study

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2013年第12期83卷 2206-2218页

作者： Tu, Chunhao Jiao, Shuo koh, Woon Yuen Univ New England Coll Pharm Portland ME 04103 USA Fred Hutchinson Canc Res Ctr Div Publ Hlth Seattle WA 98109 USA Univ New England Dept Math Sci Biddeford ME 04005 USA

In observational studies, unbalanced observed covariates between treatment groups often cause biased inferences on the estimation of treatment effects. Recently, generalized propensity score (GPS) has been proposed to overcome this problem;however, a practical technique to apply the GPS is lacking. This study demonstrates how clustering algorithms can be used to group similar subjects based on transformed GPS. We compare four popular clustering algorithms: k-means clustering (kMC), model-based clustering, fuzzy c-means clustering and partitioning around medoids based on the following three criteria: average dissimilarity between subjects within clusters, average Dunn index and average silhouette width under four various covariate scenarios. Simulation studies show that the kMC algorithm has overall better performance compared with the other three clustering algorithms. Therefore, we recommend using the kMC algorithm to group similar subjects based on the transformed GPS.

关键词： fuzzy c-means clustering algorithm generalized propensity score k-means clustering algorithm model-based clustering algorithm observational studies partitioning around medoids algorithm

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Frequency-hopping signals sorting based on underdetermined blind source separation

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IET COMMUNICATIONS 2013年第14期7卷 1456-1464页

作者： Sha, Zhi-Chao Huang, Zhi-Tao Zhou, Yi-Yu Wang, Feng-Hua Natl Univ Def Technol Coll Elect Sci & Engn Changsha 410073 Hunan Peoples R China

Most of the earlier approaches for frequency-hopping (FH) signals sorting with a single sensor do not adapt to synchronous network, whereas the multiple-sensor-based algorithms request the number of sensors must be more than that of signals. Since the number of sensors is limited in many practical applications, it is important to sort synchronous or asynchronous FH networks with as little as possible sensors. This study introduces the underdetermined blind source separation (UBSS) algorithm to solve this problem. Considering the time-frequency (TF) sparsity of FH signals, the problem is formulated as one of UBSS based on sparse TF representation. First, an improved k-means clustering algorithm is developed to estimate the mixing matrix, according to the TF properties of FH signals. Secondly, to separate more signals overlapped in the TF domain using given sensors, an improved subspace-based algorithm utilising the information of the comparative power is proposed. In the proposed method, the sparsity condition of the sources in the TF domain is relaxed, and the number of FH signals that exist at any TF point simultaneously is allowed to equal that of the sensors. Simulations demonstrate that the proposed method can separate FH signals efficiently and outperforms the previous methods.

关键词： blind source separation estimation theory frequency hop communication frequency hopping signal underdetermined blind source separation UBSS algorithm time-frequency sparsity sparse time-frequency representation k-means clustering algorithm mixing matrix estimation comparative power source sparsity condition time-frequency domain time-frequency point

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A batch-based run-to-run process control scheme for semiconductor manufacturing

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IIE TRANSACTIONS 2013年第6期45卷 658-669页

作者： Wang, kaibo Han, kai Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China

Run-to-Run (R2R) control has been widely used in semiconductor manufacturing to compensate for process disturbance and to improve quality. The traditional R2R controller only takes the process output in a previous run as its input and generates an optimal recipe for the next run. In a multistage semiconductor manufacturing process, variations in upstream stations are propagated to downstream stations. However, the information from upstream stations is not considered by existing controllers. In addition, most R2R processes have a limited capacity;the products must be processed in batches. Therefore, if the incoming materials could be grouped with small within-batch variations and large batch-to-batch variations and the recipes are customized for each batch to drive all batch averages toward the same target value, the output variation could be reduced and quality improved. A batch Exponentially Weighted Moving Average (EWMA) controller is proposed in this article. It employs a modified k-means algorithm to group the incoming materials into batches with a fixed and equal size while minimizing the within-batch variation. The controller then generates the control settings by taking both the batch information and the feedback quality information into account. Simulation studies show that the proposed controller could significantly reduce output variation and improve quality.

关键词： Batch-to-batch variation k-means clustering algorithm run-to-run process control semiconductor manufacturing within-batch variation

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Dynamic Delegation Approach in the Electronic Evidence Storage of Cloud Platform

Dynamic Delegation Approach in the Electronic Evidence Stora...

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3rd International Conference on Applied Mechanics, Materials and Manufacturing (ICAMMM 2013)

作者： Wang, Yanfei Xie, Jun Xian, Zhengguang Capital Normal Univ Coll Informat Engn Beijing Peoples R China

ISBN: (纸本)9783037858882

The trustworthiness of the web resources is very important for secure cloud platform of electronic evidence storage calculation. In this paper, I analyzed the possibility of classes of delegation and trust level of authorization access. I used CGRS resource selection algorithm which is based on the k-means clustering algorithm in digital forensics analysis of cloud platform resources, and the algorithm is used to scalable environment for digital forensics analysis platform in substantive departments. The algorithm is proved effective and correct by the analysis and the experimental results.

关键词： Electronic evidence Cloud platform k-means clustering algorithm CGRS resource selection algorithm Dynamic Access Control

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Collective Assessment of Arguments in an Online Intelligent Argumentation System for Collaborative Decision Support

Collective Assessment of Arguments in an Online Intelligent ...

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International Conference on Collaboration Technologies and Systems (CTS)

作者： Arvapally, Ravi Santosh Liu, Xiaoqing (Frank) Missouri Univ Sci & Technol Dept Comp Sci Rolla MO 65409 USA

ISBN: (纸本)9781467364041;9781467364034

We developed an intelligent argumentation system for collaborative decision support which allows stakeholders to exchange arguments, captures their rationale, and assesses aggregate thoughts on solution alternatives [1, 21]. In this article we propose and evaluate a method for computing aggregate thoughts of arguments on individual arguments, which represents individual thoughts, in an argumentation tree and further classifying arguments based on relationships between its aggregate thoughts and individual thoughts. A novel approach is developed to derive collective determination of an argument based on total support and attack that an argument receives in the argumentation tree. The collective determination value and the strength of an argument are used to analyze the relationship between aggregate thought and individual thought of arguments and cluster the arguments. The decision maker will be able to review clusters of arguments with opposing collective thoughts and supporting collective thoughts versus their individual thoughts. We have carried out an experiment to evaluate the proposed method, and the experimental results show that the proposed method is effective.

关键词： Collaborative decision support argumentation fuzzy logic aggregate thoughts individual thoughts k-means clustering algorithm intelligent argumenation system

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Unsupervised Satellite Image Segmentation using a Bivariate Beta type-II Mixture Model

Unsupervised Satellite Image Segmentation using a Bivariate ...

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IEEE 9th International Conference on Computational Cybernetics (ICCC)

作者： Ben Arab, Taher Zribi, Mourad Masmoudi, Afif Lab Informat Signal & Image Cote Opale LISIC EA 4 ULCO50 Rue Ferdinand Buisson BP 719 F-62228 Calais France Univ Sfax Fac Sci Lab Probabilities Statist Sfax Tunisia

ISBN: (纸本)9781479900633;9781479900602

This paper presents an unsupervised satellite color image segmentation approach based on Bivariate Beta type-II. Such a method could be considered as original since it uses a. k-means clustering algorithm in order to initialize the image class number. Moreover, it exploits a Bivariate Beta type-II for statistical distributions applying it for each class. Satellite image exploitation requires the use of different approaches, especially those based on the unsupervised statistical segmentation principle. Such approaches necessitate the definition of several parameters such as image class number, class variables estimation and mixture distributions. The use of statistical image attributes has allowed us to get convincing results, provided that we ensure under the condition of having an initialization step with appropriate statistical distributions. Bivariate Beta type-II associated with a. k-means clustering algorithm and Expectation-Maximization (EM) algorithm could be adapted to such a problem. For each image class, Bivariate Beta typeII attributes a specific distribution type according to different parameters. Different adapted algorithms (namely. k-means clustering algorithm, EM algorithm and Bivariate Beta type-II algorithm) are then applied to the satellite image segmentation problem. The efficiency of those combined algorithms is validated with the Mean Squared Errors (MSE), Signal to Noise Ratio (SNR) and Maximum Distance (MD).

关键词： Bivariate Beta type-II Expectation Maximization algorithm k-means clustering algorithm Mean Squared Errors Unsupervised color image segmentation

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An Improved Slope One algorithm for Collaborative Filtering

An Improved Slope One Algorithm for Collaborative Filtering

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9th International Conference on Natural Computation (ICNC)

作者： Li, Jiyun Feng, Pengcheng Lv, Juntao Donghua Univ Coll Comp Sci & Technol Shanghai Peoples R China MVS R&D Ctr Shanghai Peoples R China

ISBN: (纸本)9781467347143

The slope one scheme is a rating-based recommendation algorithm, and it is simple, efficient, easy to implement. However, the slope one scheme suffers from both data sparsity and new item problems, which seriously affect the performance of recommender systems. To solve these problems, we propose an improved slope one algorithm for collaborative filtering. In our algorithm, the new item problem is dealed with by introducing content similarity computation into the slope one scheme. According to the idea of item-based collaborative filtering algorithms, the target user's rating to the target item can be predicted based on the ratings that the target user has rated and the content similarities of items. And clustering algorithm is adopted to tackle the problem of data sparsity. By merging the set of items into several clusters based on the item rating data, the target user's rating to the target item can be predicted based on which cluster the target item belongs to. The final rating of the target user to the target item is the linear combination of the above two ratings. Experiments on the Movielens dataset show that our approach outperforms other three slope one algorithms and two traditional collaborative filtering algorithms on the prediction performance.

关键词： collaborative filtering k-means clustering algorithm slope one scheme item's content similarity

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A Novel Method for Text and Non-Text Segmentation in Document Images

A Novel Method for Text and Non-Text Segmentation in Documen...

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2nd IEEE International Conference on Communications and Signal Processing (ICCSP)

作者： Deivalakshmi, S. Palanisamy, P. Vishwanathan, Gayatri Natl Inst Technol Elect & Commun Engn Dept Tiruchirappalli 620015 Tamil Nadu India

ISBN: (纸本)9781467348669;9781467348652

Segmentation of the contents of document images into text and non-text regions is an essential pre-processing step for applications such as document analysis and classification, as well as OCR. This paper presents a novel technique to segment the document image into text and non-text regions using a combination of Wavelet-based Gray Level Co-Occurrence Matrix (GLCM) features and k-means clustering. A comparison between the performances of different wavelets in document image segmentation is also performed and tabulated. The technique was tested on a number of scanned article images from the MediaTeam Document Database and results show a marked improvement over the already existing method based on GLCM features.

关键词： Discrete Wavelet Transform Document Image Segmentation GLCM Features k-means clustering algorithm

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A new method for building roof segmentation from airborne LiDAR point cloud data

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MEASUREMENT SCIENCE AND TECHNOLOGY 2013年第9期24卷 095402. doi:10.1088/0957-0233/24/9/095402-095402. doi:10.1088/0957-0233/24/9/095402页

作者： kong, Deming Xu, Lijun Li, Xiaolu Beihang Univ Sch Instrument Sci & Optoelect Engn State Key Lab Inertial Sci & Technol Beijing 100191 Peoples R China Beihang Univ Sch Instrument Sci & Optoelect Engn Minist Educ Key Lab Precis Optomechatron Technol Beijing 100191 Peoples R China

A new method based on the combination of two kinds of clustering algorithms for building roof segmentation from airborne LiDAR (light detection and ranging) point cloud data is proposed. The k-plane algorithm is introduced to classify the laser footprints that cannot be correctly classified by the traditional k-means algorithm. High-precision classification can be obtained by combining the two aforementioned clustering algorithms. Furthermore, to improve the performance of the new segmentation method, a new initialization method is proposed to acquire the number and coordinates of the initial cluster centers for the k-means algorithm. In the proposed initialization method, the geometrical planes of a building roof are estimated from the elevation image of the building roof by using the mathematical morphology and Hough transform techniques. By calculating the number and normal vectors of the estimated geometrical planes, the number and coordinates of the initial cluster centers for the k-means algorithm are obtained. With the aid of the proposed initialization and segmentation methods, the point cloud of the building roof can be rapidly and appropriately classified. The proposed methods are validated by using both simulated and real LiDAR data.

关键词： LiDAR segmentation of point cloud k-means clustering algorithm k-plane clustering algorithm estimation of cluster centers mathematical morphology

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Identifying patients in target customer segments using a two-stage clustering-classification approach: A hospital-based assessment

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COMPUTERS IN BIOLOGY AND MEDICINE 2012年第2期42卷 213-221页

作者： Chen, You-Shyang Cheng, Ching-Hsue Lai, Chien-Jung Hsu, Cheng-Yi Syu, Han-Jhou Hwa Hsia Inst Technol Dept Informat Management New Taipei City 235 Taiwan Natl Yunlin Univ Sci & Technol Dept Informat Management Touliu 640 Yunlin Taiwan Natl Chin Yi Univ Technol Dept Distribut Management Taichung 411 Taiwan Lins ENT Clin Lugang Township 505 Changhua County Taiwan

Identifying patients in a Target Customer Segment (TCS) is important to determine the demand for, and to appropriately allocate resources for, health care services. The purpose of this study is to propose a two-stage clustering-classification model through (1) initially integrating the RFM attribute and k-means algorithm for clustering the TCS patients and (2) then integrating the global discretization method and the rough set theory for classifying hospitalized departments and optimizing health care services. To assess the performance of the proposed model, a dataset was used from a representative hospital (termed Hospital-A) that was extracted from a database from an empirical study in Taiwan comprised of 183,947 samples that were characterized by 44 attributes during 2008. The proposed model was compared with three techniques, Decision Tree, Naive Bayes, and Multilayer Perceptron, and the empirical results showed significant promise of its accuracy. The generated knowledge-based rules provide useful information to maximize resource utilization and support the development of a strategy for decision-making in hospitals. From the findings, 75 patients in the TCS, three hospital departments, and specific diagnostic items were discovered in the data for Hospital-A. A potential determinant for gender differences was found, and the age attribute was not significant to the hospital departments. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Customer relationship management (CRM) Target customer segment (TCS) k-means clustering algorithm Rough set theory (RST) Recency-Frequency-Monetary (RFM) analysis model

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