We consider the topographic clustering task and focus on the problem of its evaluation, which enables to perform model selection: topographic clustering algorithms, from the original Self Organizing Map to its extension based on kernel (STMK), can be viewed in the unified framework of constrained clustering. Exploiting this point of view, we discuss existing quality measures and we propose a ne...

Recently, multi-view clustering has received much attention in the fields of machine learning and pattern recognition. Spectral for single multiple views been common solution. Despite its good performance, it a major limitation: requires an extra step clustering. This step, which could be famous k-means clustering, depends heavily on initialization, may affect quality result. To overcome this p...

Document clustering is an important tool for applications such as Web search engines. Clustering documents enables the user to have a good overall view of the information contained in the documents that he has. However, existing algorithms sufSer from various aspects; hard clustering algorithms (where each document belongs to exactly one cluster) cannot detect the multiple themes of a document,...

In this paper the problems of deriving a taxonomy from a text and concept-oriented text segmentation are approached. Formal Concept Analysis (FCA) method is applied to solve both of these linguistic problems. The proposed segmentation method offers a conceptual view for text segmentation, using a context-driven clustering of sentences. The Concept-oriented Clustering Segmentation algorithm (COC...

Incomplete multi-view clustering (IMVC) has attracted remarkable attention due to the emergence of data with missing views in real applications. Recent methods attempt recover information address IMVC problem. However, they generally cannot fully explore underlying properties and correlations similarities across views. This paper proposes a novel Enhanced Tensor Low-rank Sparse Representation R...

More and more multi-view data which can capture rich information from heterogeneous features are widely used in real world applications. How to integrate different types of features, and how to learn low dimensional and discriminative information from high dimensional data are two main challenges. To address these challenges, this paper proposes a novel multi-view feature learning framework, wh...

Multi-view spectral clustering has drawn much attention due to the effectiveness of exploiting similarity relationships among data points. These methods typically reveal intrinsic structure using a predefined graph for each view. The graphs are fused consensus one, on which final results obtained. However, such common strategies may lead information loss because inconsistency or noise multiple ...