• Title/Summary/Keyword: model-based clustering

Search Result 643, Processing Time 0.079 seconds

A Bayesian Model-based Clustering with Dissimilarities

  • Oh, Man-Suk;Raftery, Adrian
    • Proceedings of the Korean Statistical Society Conference
    • /
    • /
    • pp.9-14
    • /
    • 2003
  • A Bayesian model-based clustering method is proposed for clustering objects on the basis of dissimilarites. This combines two basic ideas. The first is that tile objects have latent positions in a Euclidean space, and that the observed dissimilarities are measurements of the Euclidean distances with error. The second idea is that the latent positions are generated from a mixture of multivariate normal distributions, each one corresponding to a cluster. We estimate the resulting model in a Bayesian way using Markov chain Monte Carlo. The method carries out multidimensional scaling and model-based clustering simultaneously, and yields good object configurations and good clustering results with reasonable measures of clustering uncertainties. In the examples we studied, the clustering results based on low-dimensional configurations were almost as good as those based on high-dimensional ones. Thus tile method can be used as a tool for dimension reduction when clustering high-dimensional objects, which may be useful especially for visual inspection of clusters. We also propose a Bayesian criterion for choosing the dimension of the object configuration and the number of clusters simultaneously. This is easy to compute and works reasonably well in simulations and real examples.

  • PDF

Model-based Clustering of DOA Data Using von Mises Mixture Model for Sound Source Localization

  • Dinh, Quang Nguyen;Lee, Chang-Hoon
    • International Journal of Fuzzy Logic and Intelligent Systems
    • /
    • v.13 no.1
    • /
    • pp.59-66
    • /
    • 2013
  • In this paper, we propose a probabilistic framework for model-based clustering of direction of arrival (DOA) data to obtain stable sound source localization (SSL) estimates. Model-based clustering has been shown capable of handling highly overlapped and noisy datasets, such as those involved in DOA detection. Although the Gaussian mixture model is commonly used for model-based clustering, we propose use of the von Mises mixture model as more befitting circular DOA data than a Gaussian distribution. The EM framework for the von Mises mixture model in a unit hyper sphere is degenerated for the 2D case and used as such in the proposed method. We also use a histogram of the dataset to initialize the number of clusters and the initial values of parameters, thereby saving calculation time and improving the efficiency. Experiments using simulated and real-world datasets demonstrate the performance of the proposed method.

Clustering-based identification for the prediction of splitting tensile strength of concrete

  • Tutmez, Bulent
    • Computers and Concrete
    • /
    • v.6 no.2
    • /
    • pp.155-165
    • /
    • 2009
  • Splitting tensile strength (STS) of high-performance concrete (HPC) is one of the important mechanical properties for structural design. This property is related to compressive strength (CS), water/binder (W/B) ratio and concrete age. This paper presents a clustering-based fuzzy model for the prediction of STS based on the CS and (W/B) at a fixed age (28 days). The data driven fuzzy model consists of three main steps: fuzzy clustering, inference system, and prediction. The system can be analyzed directly by the model from measured data. The performance evaluations showed that the fuzzy model is more accurate than the other prediction models concerned.

Cluster-based Information Retrieval with Tolerance Rough Set Model

  • Ho, Tu-Bao;Kawasaki, Saori;Nguyen, Ngoc-Binh
    • International Journal of Fuzzy Logic and Intelligent Systems
    • /
    • v.2 no.1
    • /
    • pp.26-32
    • /
    • 2002
  • The objectives of this paper are twofold. First is to introduce a model for representing documents with semantics relatedness using rough sets but with tolerance relations instead of equivalence relations (TRSM). Second is to introduce two document hierarchical and nonhierarchical clustering algorithms based on this model and TRSM cluster-based information retrieval using these two algorithms. The experimental results show that TRSM offers an alterative approach to text clustering and information retrieval.

A Density-based Clustering Method

  • Ahn, Sung Mahn;Baik, Sung Wook
    • Communications for Statistical Applications and Methods
    • /
    • v.9 no.3
    • /
    • pp.715-723
    • /
    • 2002
  • This paper is to show a clustering application of a density estimation method that utilizes the Gaussian mixture model. We define "closeness measure" as a clustering criterion to see how close given two Gaussian components are. Closeness measure is defined as the ratio of log likelihood between two Gaussian components. According to simulations using artificial data, the clustering algorithm turned out to be very powerful in that it can correctly determine clusters in complex situations, and very flexible in that it can produce different sizes of clusters based on different threshold valuesold values

Curve Clustering in Microarray

  • Lee, Kyeong-Eun
    • Journal of the Korean Data and Information Science Society
    • /
    • v.15 no.3
    • /
    • pp.575-584
    • /
    • 2004
  • We propose a Bayesian model-based approach using a mixture of Dirichlet processes model with discrete wavelet transform, for curve clustering in the microarray data with time-course gene expressions.

  • PDF

Fine-Grained Mobile Application Clustering Model Using Retrofitted Document Embedding

  • Yoon, Yeo-Chan;Lee, Junwoo;Park, So-Young;Lee, Changki
    • ETRI Journal
    • /
    • v.39 no.4
    • /
    • pp.443-454
    • /
    • 2017
  • In this paper, we propose a fine-grained mobile application clustering model using retrofitted document embedding. To automatically determine the clusters and their numbers with no predefined categories, the proposed model initializes the clusters based on title keywords and then merges similar clusters. For improved clustering performance, the proposed model distinguishes between an accurate clustering step with titles and an expansive clustering step with descriptions. During the accurate clustering step, an automatically tagged set is constructed as a result. This set is utilized to learn a high-performance document vector. During the expansive clustering step, more applications are then classified using this document vector. Experimental results showed that the purity of the proposed model increased by 0.19, and the entropy decreased by 1.18, compared with the K-means algorithm. In addition, the mean average precision improved by more than 0.09 in a comparison with a support vector machine classifier.

Online nonparametric Bayesian analysis of parsimonious Gaussian mixture models and scenes clustering

  • Zhou, Ri-Gui;Wang, Wei
    • ETRI Journal
    • /
    • v.43 no.1
    • /
    • pp.74-81
    • /
    • 2021
  • The mixture model is a very powerful and flexible tool in clustering analysis. Based on the Dirichlet process and parsimonious Gaussian distribution, we propose a new nonparametric mixture framework for solving challenging clustering problems. Meanwhile, the inference of the model depends on the efficient online variational Bayesian approach, which enhances the information exchange between the whole and the part to a certain extent and applies to scalable datasets. The experiments on the scene database indicate that the novel clustering framework, when combined with a convolutional neural network for feature extraction, has meaningful advantages over other models.

Neuro-Fuzzy Modeling based on Self-Organizing Clustering (자기구성 클러스터링 기반 뉴로-퍼지 모델링)

  • Kim Sung-Suk;Ryu Jeong-Woong;Kim Yong-Tae
    • Journal of the Korean Institute of Intelligent Systems
    • /
    • v.15 no.6
    • /
    • pp.688-694
    • /
    • 2005
  • In this Paper, we Propose a new neuro-fuzzy modeling using clustering-based learning method. In the proposed clustering method, number of clusters is automatically inferred and its parameters are optimized simultaneously, Also, a neuro-fuzzy model is learned based on clustering information at same time. In the previous modelling method, clustering and model learning are performed independently and have no exchange of its informations. However, in the proposed method, overall neuro-fuzzy model is generated by using both clustering and model learning, and the information of modelling output is used to clustering of input. The proposed method improve the computational load of modeling using Subtractive clustering method. Simulation results show that the proposed method has an effectiveness compared with the previous methods.

Determining on Model-based Clusters of Time Series Data (시계열데이터의 모델기반 클러스터 결정)

  • Jeon, Jin-Ho;Lee, Gye-Sung
    • The Journal of the Korea Contents Association
    • /
    • v.7 no.6
    • /
    • pp.22-30
    • /
    • 2007
  • Most real word systems such as world economy, stock market, and medical applications, contain a series of dynamic and complex phenomena. One of common methods to understand these systems is to build a model and analyze the behavior of the system. In this paper, we investigated methods for best clustering over time series data. As a first step for clustering, BIC (Bayesian Information Criterion) approximation is used to determine the number of clusters. A search technique to improve clustering efficiency is also suggested by analyzing the relationship between data size and BIC values. For clustering, two methods, model-based and similarity based methods, are analyzed and compared. A number of experiments have been performed to check its validity using real data(stock price). BIC approximation measure has been confirmed that it suggests best number of clusters through experiments provided that the number of data is relatively large. It is also confirmed that the model-based clustering produces more reliable clustering than similarity based ones.