Analysis of Semi-supervised Text Clustering Algorithm on Marine Data
نویسندگان
چکیده
منابع مشابه
A Semi - supervised Text Clustering Algorithm Based on Pairwise Constraints ★
In this paper, an active learning method which can effectively select pairwise constraints during clustering procedure was presented. A novel semi-supervised text clustering algorithm was proposed, which employed an effective pairwise constraints selection method. As the samples on the fuzzy boundary are far away from the cluster center in the clustering procedure, they can be easily divided in...
متن کاملSemi supervised clustering for Text Clustering
ABSTRACT: Based on clustering algorithm Affinity Propagation (AP) I present this paper a semisupervised text clustering algorithm, called Seeds Affinity Propagation (SAP). There are two main contributions in my approach: 1) a similarity metric that captures the structural information of texts, and 2) seed construction method to improve the semisupervised clustering process. To study the perform...
متن کاملSemi-supervised Clustering of Medical Text
Semi-supervised clustering is an attractive alternative for traditional (unsupervised) clustering in targeted applications. By using the information of a small annotated dataset, semi-supervised clustering can produce clusters that are customized to the application domain. In this paper, we present a semi-supervised clustering technique based on a multi-objective evolutionary algorithm (NSGA-II...
متن کاملSemi-Supervised Learning for Web Text Clustering
Supervised learning algorithms usually require large amounts of training data to learn reasonably accurate classifiers. Yet, for many text classification tasks, providing labeled training documents is expensive, while unlabeled documents are readily available in large quantities. Learning from both, labeled and unlabeled documents, in a semi-supervised framework is a promising approach to reduc...
متن کاملTri-training and Data Editing Based Semi-supervised Clustering Algorithm
Semi-Supervised clustering algorithms often utilize a seeds set consisting of a small amount of labeled data to initialize cluster centroids, hence improve the clustering performance over whole data set. Both the scale and quality of seeds set directly restrict the performance of semi-supervised clustering algorithm. In this paper, a new algorithm named DE-Tri-training semi-supervised K-means i...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computers, Materials & Continua
سال: 2020
ISSN: 1546-2226
DOI: 10.32604/cmc.2020.09861