• 제목/요약/키워드: clustering model

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Fine-Grained Mobile Application Clustering Model Using Retrofitted Document Embedding

  • Yoon, Yeo-Chan;Lee, Junwoo;Park, So-Young;Lee, Changki
    • ETRI Journal
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    • 제39권4호
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    • pp.443-454
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    • 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.

A Bayesian Model-based Clustering with Dissimilarities

  • Oh, Man-Suk;Raftery, Adrian
    • 한국통계학회:학술대회논문집
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    • 한국통계학회 2003년도 추계 학술발표회 논문집
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    • pp.9-14
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    • 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.

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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
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    • 제13권1호
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    • pp.59-66
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    • 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.

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

  • Zhou, Ri-Gui;Wang, Wei
    • ETRI Journal
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    • 제43권1호
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    • pp.74-81
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    • 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.

Prediction of Energy Consumption in a Smart Home Using Coherent Weighted K-Means Clustering ARIMA Model

  • Magdalene, J. Jasmine Christina;Zoraida, B.S.E.
    • International Journal of Computer Science & Network Security
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    • 제22권10호
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    • pp.177-182
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    • 2022
  • Technology is progressing with every passing day and the enormous usage of electricity is becoming a necessity. One of the techniques to enjoy the assistances in a smart home is the efficiency to manage the electric energy. When electric energy is managed in an appropriate way, it drastically saves sufficient power even to be spent during hard time as when hit by natural calamities. To accomplish this, prediction of energy consumption plays a very important role. This proposed prediction model Coherent Weighted K-Means Clustering ARIMA (CWKMCA) enhances the weighted k-means clustering technique by adding weights to the cluster points. Forecasting is done using the ARIMA model based on the centroid of the clusters produced. The dataset for this proposed work is taken from the Pecan Project in Texas, USA. The level of accuracy of this model is compared with the traditional ARIMA model and the Weighted K-Means Clustering ARIMA Model. When predicting,errors such as RMSE, MAPE, AIC and AICC are analysed, the results of this suggested work reveal lower values than the ARIMA and Weighted K-Means Clustering ARIMA models. This model also has a greater loglikelihood, demonstrating that this model outperforms the ARIMA model for time series forecasting.

Inter-clustering Cooperative Relay Selection Schemes for 5G Device-to-device Communication Networks

  • Nasaruddin, Nasaruddin;Yunida, Yunida;Adriman, Ramzi
    • Journal of information and communication convergence engineering
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    • 제20권3호
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    • pp.143-152
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    • 2022
  • The ongoing adoption of 5G will increase the data traffic, throughput, multimedia services, and power consumption for future wireless applications and services, including sensor and mobile networks. Multipath fading on wireless channels also reduces the system performance and increases energy consumption. To address these issues, device-to-device (D2D) and cooperative communications have been proposed. In this study, we propose two inter-clustering models using the relay selection method to improve system performance and increase energy efficiency in cooperative D2D networks. We develop two inter-clustering models and present their respective algorithms. Subsequently, we run a computer simulation to evaluate each model's outage probability (OP) performance, throughput, and energy efficiency. The simulation results show that inter-clustering model II has the lowest OP, highest throughput, and highest energy efficiency compared with inter-clustering model I and the conventional inter-clustering-based multirelay method. These results demonstrate that inter-clustering model II is well-suited for use in 5G overlay D2D and cellular communications.

Curve Clustering in Microarray

  • Lee, Kyeong-Eun
    • Journal of the Korean Data and Information Science Society
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    • 제15권3호
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    • pp.575-584
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    • 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.

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

  • 김승석;유정웅;김용태
    • 한국지능시스템학회논문지
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    • 제15권6호
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    • pp.688-694
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    • 2005
  • 본 논문에서는 클러스터링을 뉴로-퍼지 모델에 직접 적용하여 모델을 최적화하는 방법을 제안하였다. 기존의 오차미분기반 학습을 통한 뉴로-퍼지 모델의 최적화 과정과는 달리 제안된 방법은 클러스터링 학습과 연계하여 모델을 구성하며 자율적으로 클러스터의 수를 추정하며 동시에 최적화를 수행한다. 순차적인 학습 기법에서는 각각의 학습 기법을 따로 적용하여 모델링을 실시하였으나 제안된 기법에서는 하나의 클러스터링 학습으로 전체 모델의 학습을 실시하였다. 또한 제안된 방법에서는 클러스터링이 수렴하는 만큼 전체 모델의 연산량이 감소하여 학습과정에서 발생하는 연산량 문제를 개선하였다. 시뮬레이션을 통하여 기존의 연구 결과들과 비교하여 제안된 기법의 유용성을 보였다.

Nonlinear structural finite element model updating with a focus on model uncertainty

  • Mehrdad, Ebrahimi;Reza Karami, Mohammadi;Elnaz, Nobahar;Ehsan Noroozinejad, Farsangi
    • Earthquakes and Structures
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    • 제23권6호
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    • pp.549-580
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    • 2022
  • This paper assesses the influences of modeling assumptions and uncertainties on the performance of the non-linear finite element (FE) model updating procedure and model clustering method. The results of a shaking table test on a four-story steel moment-resisting frame are employed for both calibrations and clustering of the FE models. In the first part, simple to detailed non-linear FE models of the test frame is calibrated to minimize the difference between the various data features of the models and the structure. To investigate the effect of the specified data feature, four of which include the acceleration, displacement, hysteretic energy, and instantaneous features of responses, have been considered. In the last part of the work, a model-based clustering approach to group models of a four-story frame with similar behavior is introduced to detect abnormal ones. The approach is a composition of property derivation, outlier removal based on k-Nearest neighbors, and a K-means clustering approach using specified data features. The clustering results showed correlations among similar models. Moreover, it also helped to detect the best strategy for modeling different structural components.

FCM 클러스터링 알고리즘과 퍼지 결정트리를 이용한 상황인식 정보 서비스 (A Context-Aware Information Service using FCM Clustering Algorithm and Fuzzy Decision Tree)

  • 양석환;정목동
    • 한국멀티미디어학회논문지
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    • 제16권7호
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    • pp.810-819
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    • 2013
  • FCM 클러스터링 알고리즘은 대표적인 분할기반 군집화 알고리즘이며 다양한 분야에서 성공적으로 적용되어 왔다. 그러나 FCM 클러스터링 알고리즘은 잡음 및 지역 데이터에 대한 높은 민감도, 직관적인 결과와 상이한 결과 도출 가능성이 높은 문제, 초기 원형과 클러스터 개수 설정 문제 등이 존재한다. 본 논문에서는 FCM 알고리즘의 결과를 해당 속성의 데이터 축에 사상하여 퍼지구간을 결정하고, 결정된 퍼지구간을 FDT에 적용함으로써 FCM 알고리즘이 가지는 문제 중 잡음 및 데이터에 대한 높은 민감도, 직관적인 결과와 상이한 결과 도출 가능성이 높은 문제를 개선하는 시스템을 제안한다. 또한 실제 교통데이터와 강수량 데이터를 이용한 실험을 통하여 제안 모델과 FCM 클러스터링 알고리즘을 비교한다. 실험 결과를 통해 제안 모델은 잡음 및 데이터에 대한 민감도를 완화시킴으로써 보다 안정적인 결과를 제공하며, FCM 클러스터링 알고리즘을 적용한 시스템보다 직관적인 결과와의 일치율을 높여줌을 알 수 있다.