Recently, semi-supervised spectral clustering algorithms have been developing rapidly, which are proposed to improve the clustering performance. In this paper, we first review the current existing spectral clustering algorithms in an unified-framework and give a straightforward explanation about the spectral clustering algorithm. Then, we present a semi-supervised method to improve the clusteri...

Both the instance level knowledge and the attribute level knowledge can improve clustering quality, but how to effectively utilize both of them is an essential problem to solve. This paper proposes a wrapper framework for semi-supervised clustering, which aims to gracely integrate both kinds of priori knowledge in the 598 J. L. Wang, S.Y. Wu, C. Wen, G. Li clustering process, the instance level...

Chatbots represent a promising tool to automate the processing of requests in business context. However, despite major progress natural language technologies, constructing dataset deemed relevant by experts is manual, iterative and error-prone process. To assist these during modelling labelling, authors propose an active learning methodology coined Interactive Clustering. It relies on interacti...

The huge amount of currently available data puts considerable constraints on the task of information retrieval. Automatic methods to organize data, such as clustering, can be used to help with this task allowing timely access. Semi-supervised clustering approaches employ some additional information to guide the clustering performed based on data attributes to a more suitable data partition. How...

In our study on developing a text mining prototype system, it is needed to group documents according to author’s need. However, Traditional documents clustering are usually considered an unsupervised learning. It cannot effectively group documents under user’s need. To solve this problem, we propose a new documents clustering approach. The main contributions include: (1) Describes user’s need b...

We propose a Classification Via Clustering (CVC) algorithm which enables existing clustering methods to be efficiently employed in classification problems. In CVC, training and test data are coclustered and class-cluster distributions are used to find the label of the test data. To determine an efficient number of clusters, a Semi-supervised Hierarchical Clustering (SHC) algorithm is proposed. ...

We present a variational inference scheme for semi-supervised clustering in which data is supplemented with side information in the form of common labels. There is no mutual exclusion of classes assumption and samples are represented as a combinatorial mixture over multiple clusters. The method has other advantages such as the ability to find the most probable number of soft clusters in the dat...

Semi-supervised active clustering (SSAC) utilizes the knowledge of a domain expert to cluster data points by interactively making pairwise “same-cluster” queries. However, it is impractical to ask human oracles to answer every pairwise query. In this paper, we study the influence of allowing “not-sure” answers from a weak oracle and propose algorithms to efficiently handle uncertainties. Differ...