A novel learning and prediction Bayesian hierarchical clustering-Dirichlet mixture model for effective data mining

In this paper, we propose a novel hybrid discriminative learning approach based on shifted-scaled Dirichlet mixture model (SSDMM) and Support Vector Machines (SVMs) to address some challenging problems of medical data categorization and recognition. The main goal is to capture accurately the intrinsic nature of biomedical images by considering the desirable properties of both generative and discriminative models. To achieve this objective, we propose to derive new data-based SVM kernels generated from the developed mixture model SSDMM. The proposed approach includes the following steps: the extraction of robust local descriptors, the learning of the developed mixture model via the expectation–maximization (EM) algorithm, and finally the building of three SVM kernels for data categorization and classification. The potential of the implemented framework is illustrated through two challenging problems that concern the categorization of retinal images into normal or diabetic cases and the recognition of lung diseases in chest X-rays (CXR) images. The obtained results demonstrate the merits of our hybrid approach as compared to other methods.

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Generalized additive multi-mixture model for data mining

Computational Statistics & Data Analysis ◽

10.1016/s0167-9473(01)00074-3 ◽

2002 ◽

Vol 38 (4) ◽

pp. 487-500 ◽

Cited By ~ 4

Author(s):

Claudio Conversano ◽

Roberta Siciliano ◽

Francesco Mola

Keyword(s):

Data Mining ◽

Mixture Model

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2P132 Hydration water behavior classified by mixture model : Simulation data-mining approach(07. Water & Hydration & Electrolyte,Poster,The 52nd Annual Meeting of the Biophysical Society of Japan(BSJ2014))

Seibutsu Butsuri ◽

10.2142/biophys.54.s216_6 ◽

2014 ◽

Vol 54 (supplement1-2) ◽

pp. S216

Author(s):

Taku Mizukami ◽

Hieu Chi Dam ◽

Tu Bao Ho ◽

Viet Cuong Nguen

Keyword(s):

Data Mining ◽

Annual Meeting ◽

Mixture Model ◽

Model Simulation ◽

Hydration Water ◽

Simulation Data ◽

Data Mining Approach ◽

Biophysical Society ◽

Water Behavior

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Improved minimum-minimum roughness algorithm for clustering categorical data

International Journal of ADVANCED AND APPLIED SCIENCES ◽

10.21833/ijaas.2021.10.006 ◽

2021 ◽

Vol 8 (10) ◽

pp. 43-50

Author(s):

Truong et al. ◽

Keyword(s):

Machine Learning ◽

Data Mining ◽

Hierarchical Clustering ◽

Categorical Data ◽

Clustering Algorithm ◽

Clustering Algorithms ◽

Experimental Results ◽

Data Sets ◽

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Hierarchical Clustering Algorithm

Clustering is a fundamental technique in data mining and machine learning. Recently, many researchers are interested in the problem of clustering categorical data and several new approaches have been proposed. One of the successful and pioneering clustering algorithms is the Minimum-Minimum Roughness algorithm (MMR) which is a top-down hierarchical clustering algorithm and can handle the uncertainty in clustering categorical data. However, MMR tends to choose the category with less value leaf node with more objects, leading to undesirable clustering results. To overcome such shortcomings, this paper proposes an improved version of the MMR algorithm for clustering categorical data, called IMMR (Improved Minimum-Minimum Roughness). Experimental results on actual data sets taken from UCI show that the IMMR algorithm outperforms MMR in clustering categorical data.

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