An entropy weighting mixture model for subspace clustering of high-dimensional data

Data classification is an important research topic in the field of data mining. With the rapid development in social media sites and IoT devices, data have grown tremendously in volume and complexity, which has resulted in a lot of large and complex high-dimensional data. Classifying such high-dimensional complex data with a large number of classes has been a great challenge for current state-of-the-art methods. This paper presents a novel, hierarchical, gamma mixture model-based unsupervised method for classifying high-dimensional data with a large number of classes. In this method, we first partition the features of the dataset into feature strata by using k-means. Then, a set of subspace data sets is generated from the feature strata by using the stratified subspace sampling method. After that, the GMM Tree algorithm is used to identify the number of clusters and initial clusters in each subspace dataset and passing these initial cluster centers to k-means to generate base subspace clustering results. Then, the subspace clustering result is integrated into an object cluster association (OCA) matrix by using the link-based method. The ensemble clustering result is generated from the OCA matrix by the k-means algorithm with the number of clusters identified by the GMM Tree algorithm. After producing the ensemble clustering result, the dominant class label is assigned to each cluster after computing the purity. A classification is made on the object by computing the distance between the new object and the center of each cluster in the classifier, and the class label of the cluster is assigned to the new object which has the shortest distance. A series of experiments were conducted on twelve synthetic and eight real-world data sets, with different numbers of classes, features, and objects. The experimental results have shown that the new method outperforms other state-of-the-art techniques to classify data in most of the data sets.

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The Generalized Bayes Method for High-Dimensional Data Recognition with Applications to Audio Signal Recognition

Symmetry ◽

10.3390/sym13010019 ◽

2020 ◽

Vol 13 (1) ◽

pp. 19

Author(s):

Hsiuying Wang

Keyword(s):

Gaussian Mixture Model ◽

Mixture Model ◽

Conventional Method ◽

High Dimensional Data ◽

Audio Signal ◽

Gaussian Mixture ◽

High Dimensional ◽

Signal Recognition ◽

Bayes Method ◽

Generalized Bayes

High-dimensional data recognition problem based on the Gaussian Mixture model has useful applications in many area, such as audio signal recognition, image analysis, and biological evolution. The expectation-maximization algorithm is a popular approach to the derivation of the maximum likelihood estimators of the Gaussian mixture model (GMM). An alternative solution is to adopt a generalized Bayes estimator for parameter estimation. In this study, an estimator based on the generalized Bayes approach is established. A simulation study shows that the proposed approach has a performance competitive to that of the conventional method in high-dimensional Gaussian mixture model recognition. We use a musical data example to illustrate this recognition problem. Suppose that we have audio data of a piece of music and know that the music is from one of four compositions, but we do not know exactly which composition it comes from. The generalized Bayes method shows a higher average recognition rate than the conventional method. This result shows that the generalized Bayes method is a competitor to the conventional method in this real application.

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A meta-heuristic density-based subspace clustering algorithm for high-dimensional data

Soft Computing ◽

10.1007/s00500-021-05973-1 ◽

2021 ◽

Author(s):

Parul Agarwal ◽

Shikha Mehta ◽

Ajith Abraham

Keyword(s):

Clustering Algorithm ◽

High Dimensional Data ◽

Subspace Clustering ◽

High Dimensional

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Subspace Clustering of High Dimensional Data Using Differential Evolution

Nature-Inspired Algorithms for Big Data Frameworks - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-5852-1.ch003 ◽

2019 ◽

pp. 47-74 ◽

Cited By ~ 1

Author(s):

Parul Agarwal ◽

Shikha Mehta

Keyword(s):

Differential Evolution ◽

Distance Measure ◽

Dimensional Space ◽

Clustering Algorithms ◽

High Dimensional Data ◽

Subspace Clustering ◽

High Dimensional ◽

Dbscan Clustering ◽

Evolution Algorithms ◽

Self Adaptive

Subspace clustering approaches cluster high dimensional data in different subspaces. It means grouping the data with different relevant subsets of dimensions. This technique has become very effective as a distance measure becomes ineffective in a high dimensional space. This chapter presents a novel evolutionary approach to a bottom up subspace clustering SUBSPACE_DE which is scalable to high dimensional data. SUBSPACE_DE uses a self-adaptive DBSCAN algorithm to perform clustering in data instances of each attribute and maximal subspaces. Self-adaptive DBSCAN clustering algorithms accept input from differential evolution algorithms. The proposed SUBSPACE_DE algorithm is tested on 14 datasets, both real and synthetic. It is compared with 11 existing subspace clustering algorithms. Evaluation metrics such as F1_Measure and accuracy are used. Performance analysis of the proposed algorithms is considerably better on a success rate ratio ranking in both accuracy and F1_Measure. SUBSPACE_DE also has potential scalability on high dimensional datasets.

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