A Comparison of Fuzzy Clustering Approaches for Quantification of Microarray Gene Expression

Wang, Yuping; Gunampally, Maheswar; Chen, Jie; Bittel, Douglas C.; Butler, Merlin G.; Cai, Wei-Wen

doi:10.1007/s11265-007-0123-0

Cited by 4 publications

(6 citation statements)

References 28 publications

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“…BFCM-FD is reproducible, inheriting this feature from BC [12]. In addition, BFCM-FD, inherits from FCM-FD the capability of providing the best performance in stability criterion in comparison to hard methods [96]. BFCM-FD allows respondents to belong to more than one cluster.…”

Section: Theoretical Implicationsmentioning

confidence: 99%

Bagged fuzzy clustering for fuzzy data: An application to a tourism market

D’Urso

Disegna

Massari

et al. 2015

Knowledge-Based Systems

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Segmentation has several strategic and tactical implications in marketing products and services. Despite hard clustering methods having several weaknesses, they remain widely applied in marketing studies. Alternative segmentation methods such as fuzzy methods are rarely used to understand consumer behaviour. In this study, we propose a strategy of analysis, by combining the Bagged Clustering (BC) method and the fuzzy C-means clustering method for fuzzy data (FCM-FD), i.e., the Bagged fuzzy C-means clustering method for fuzzy data (BFCM-FD). The method inherits the advantages of stability and reproducibility from BC and the flexibility from FCM-FD. The method is applied on a sample of 328 Chinese consumers revealing the existence of four segments (Admirers, Enthusiasts, Moderates, and Apathetics) of the perceived images of Western Europe as a tourist destination. The results highlight the heterogeneity in Chinese consumers' place preferences and implications for place marketing are offered.

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Section: Theoretical Implicationsmentioning

confidence: 99%

Bagged fuzzy clustering for fuzzy data: An application to a tourism market

D’Urso

Disegna

Massari

et al. 2015

Knowledge-Based Systems

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“…Table 4 shows average result obtained for a simulated microarray image with 10 replicates obtained by adding the noise with the same SNR value 19 .…”

Section: Results Of Simulated Microarray Databasementioning

confidence: 99%

“…SSD calculates the variation in the log ratio estimate. A smaller value of SSD or less variation shows stability of the method 19 . SSD is used to find the stability of the estimated gene expression levels obtained using the proposed algorithms.…”

Section: Nmsementioning

confidence: 99%

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Possibilistic Reformed Fuzzy Local Information Clustering Technique for Noisy Microarray Image Spots Segmentation

Biju¹,

Mythili²

2017

Current Science

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The cDNA microarray image provides useful information about thousands of gene expressions simultaneously. This information is used by bioinformatics researchers for diagnosis of different diseases and drug designs. Microarray image spot segmentation using an improved fuzzy clustering algorithm is proposed in this article. The proposed Possibilistic Reformed Fuzzy Local Information C Means (PRFLICM) algorithm is a variant of Possibilistic Fuzzy Local Information C Means (PFLICM) algorithm. The parameters used for testing the proposed algorithm include segmentation matching factor (SMF), probability of error (p e ), discrepancy distance (D), normalized mean square error and sum of square distance (SSD). The performance of the algorithm is validated with a set of simulated cDNA microarray images with known gene expression values. From the results of SMF, the proposed PRFLICM shows an improvement of 0.4% and 0.1% for high noise and low noise microarray images respectively when compared to PFLICM algorithm. The proposed algorithm is applied to yeast microarray database (YMD) and is used to find the yeast cell life cycle generated genes. The results show that the proposed algorithm has identified 101 cell life cycle regulated genes out of 104 such genes published in the YMD database.Keywords: Fuzzy clustering, gene expression, image processing, microarray.cDNA microarray technology helps molecular biologists to measure simultaneously the activity of thousands of biomolecules in the cell under different experimental conditions [1][2][3] . This powerful tool in biotechnology has been utilized in many biomedical applications such as cancer research, infectious disease diagnosis, toxicology research, pharmacology research and agricultural development. Spots foreground (FG) from microarray image are segmented from the background (BG) to compute gene expression (GE). The stages involved in microarray image processing are gridding, segmentation, information extraction and GE computation.These operations are used to find the accurate location of each spot, separate each spot FG from BG and compute GE value. The log to the base 2 value of the ratio of mean or median red and green plane intensities of each spot FG is the GE value.Zacharia and Maroulis 4 proposed a 3D model for microarray spot segmentation where a 3D model was used to represent spot in a 3D space. The 3D space was optimized using genetic algorithm.Athanasiadis et al. 5,6 proposed two algorithms, fuzzy gaussian mixture model (FGMM) and wavelet markov random field (WMRF) model for segmenting microarray spots. The methods were applied on both simulated and real microarray images.Uslan et al. 7 used two clustering methods, Fuzzy CMeans (FCM) and K-means algorithms for segmentation of microarray image spots. Results showed that FCM could segment spots more accurately than K-means algorithm, but the segmentation accuracy of FCM was poor in medium and high noise spots.The Genetic Algorithm based Fuzzy C Means (GAFCM) 8 method was applied to cDNA microarray images for s...

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“…In this scenario, an incorrect association may result in error propagation and adversely influence the subsequent clustering result. In this sense, their main weakness lies in the lack of robustness [12].…”

Section: Introductionmentioning

confidence: 99%

A KNN-Scoring Based Core-Growing Approach to Cluster Analysis

Hsieh

Taur

Kung

2009

J Sign Process Syst Sign Image Video Technol

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This paper proposes a novel core-growing (CG) clustering method based on scoring k-nearest neighbors (CG-KNN). First, an initial core for each cluster is obtained, and then a tree-like structure is constructed by sequentially absorbing data points into the existing cores according to the KNN linkage score. The CG-KNN can deal with arbitrary cluster shapes via the KNN linkage strategy. On the other hand, it allows the membership of a previously assigned training pattern to be changed to a more suitable cluster. This is supposed to enhance the robustness. Experimental results on four UCI real data benchmarks and Leukemia data sets indicate that the proposed CG-KNN algorithm outperforms several popular clustering algorithms, such as Fuzzy C-means (FCM) (Xu and Wunsch IEEE Transactions on Neural Networks 16:645-678, 2005), Hierarchical Clustering (HC) (Xu and Wunsch IEEE Transactions on Neural Networks 16:645-678, 2005), Self-Organizing Maps (SOM) (Golub et al. Science 286:531-537, 1999; Tamayo et al. Proceedings of the National Academy of Science USA 96:2907, 1999), and Non-Euclidean Norm FCM (NEFCM) (Karayiannis and Randolph-Gips IEEE Transactions On Neural Networks 16, 2005)

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A Comparison of Fuzzy Clustering Approaches for Quantification of Microarray Gene Expression

Cited by 4 publications

References 28 publications

Bagged fuzzy clustering for fuzzy data: An application to a tourism market

Bagged fuzzy clustering for fuzzy data: An application to a tourism market

Possibilistic Reformed Fuzzy Local Information Clustering Technique for Noisy Microarray Image Spots Segmentation

A KNN-Scoring Based Core-Growing Approach to Cluster Analysis

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