Abstract
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.
Highlights
The classification of data is an important research topic in the field of data mining [1,2,3,4,5,6]
The link-based method [36] is used to integrate the clustering results generated from each subspace dataset into an object cluster association (OCA) matrix, on which the k-means algorithm is used to produce the ensemble clustering result with the number of clusters identified by the Gamma Mixture Models (GMMs) Tree algorithm
We first used the GMM Tree to find the number of feature strata, and a k-means algorithm to divide the set of features of the dataset into feature strata
Summary
The classification of data is an important research topic in the field of data mining [1,2,3,4,5,6]. The major issue with these techniques is the poor performance of classifying high-dimensional data with a large number of classes in terms of classification accuracy and computation cost To solve this key issue of classifying high-dimensional data with a large number of classes, we propose a new Hierarchical Gamma Mixture Model-based Unsupervised Method in this paper. In this hierarchical method, we apply a subspace ensemble approach to deal with this challenging problem by integrating multiple techniques in an innovative solution, named as the Stratified Subspace. We integrate the multiple techniques of stratified sampling, subspace clustering, GMM Tree, k-means, and the link-based approach in an innovative algorithm to solve the challenging problem of classifying the high-dimensional complex data with the curse of dimensionality characteristics and a large number of classes.
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