Active Learning for Constrained Document Clustering with Uncertainty Region

M A Balafar,R Hazratgholizadeh,M R F Derakhshi

doi:10.1155/2020/3207306

Abstract

Constrained clustering is intended to improve accuracy and personalization based on the constraints expressed by an Oracle. In this paper, a new constrained clustering algorithm is proposed and some of the informative data pairs are selected during an iterative process. Then, they are presented to the Oracle and their relation is answered with “Must-link (ML) or Cannot-link (CL).” In each iteration, first, the support vector machine (SVM) is utilized based on the label produced by the current clustering. According to the distance of each document from the hyperplane, the distance matrix is created. Also, based on cosine similarity of word2vector of each document, the similarity matrix is created. Two types of probability (similarity and degree of similarity) are calculated and they are smoothed for belonging to neighborhoods. Neighborhoods form the samples that are labeled by Oracle, to be in the same cluster. Finally, at the end of each iteration, the data with a greater level of uncertainty (in term of probability) is selected for questioning the oracle. In order to evaluate, the proposed method is compared with famous state-of-the-art methods based on two criteria and over a standard dataset. The result demonstrates an increased accuracy and stability of the obtained result with fewer questions.

Highlights

Clustering is one of the main important methods in the background of machine learning [1] and can be applied to different datasets such as the document set
If the information is presented as pairwise constraints (where a document pair must be in the same cluster (ML), while a document pair should not be located in the same cluster (CL)), and these pairwise constraints are used in the process of clustering, this method will be called pairwise constrained clustering [6, 14, 15]
Active learning in clustering is used with limitation. e aim of these methods is to select a data pair not correctly clustered by the current clustering most of the time [14, 21,22,23]

Summary

Introduction

Clustering is one of the main important methods in the background of machine learning [1] and can be applied to different datasets such as the document set. It is important to select a valuable data pair as an informative pairwise constraint. Active learning selects the informative pairwise constraints and sends them to Oracle for responding (Must-link/Cannot-Link) [15]. Active learning offers the greatest improvement and accuracy in clustering by saving time and cost for the minimum number of pairwise constraints [1, 11, 14, 17,18,19,20]. E aim of these methods is to select a data pair not correctly clustered by the current clustering most of the time [14, 21,22,23] Active learning in clustering is used with limitation. e aim of these methods is to select a data pair not correctly clustered by the current clustering most of the time [14, 21,22,23]

Methods

Results

Conclusion