What question did this study set out to answer?

The study aims to enhance Multi-View Clustering (MVC) by addressing challenges related to heterogeneous features and noise in individual views.

July 28, 2017Open Access

From Ensemble Clustering to Multi-View Clustering

Key Points

The study aims to enhance Multi-View Clustering (MVC) by addressing challenges related to heterogeneous features and noise in individual views.
Developed a Multi-View Ensemble Clustering (MVEC) framework integrating Basic Partitions (BPs) from different views.
Used low-rank and sparse decomposition to manage view connections and noise detection.
Conducted experiments on six real-world datasets to evaluate the performance against traditional MVC and EC methods.
MVEC showed significant improvement in clustering performance compared to traditional MVC methods in noisy environments.
The use of low-rank and sparse techniques effectively reduced the impact of noise on clustering accuracy.
Achieved better consensus partitions that leverage complementary information across multiple views.

Abstract

Multi-View Clustering (MVC) aims to find the cluster structure shared by multiple views of a particular dataset. Existing MVC methods mainly integrate the raw data from different views, while ignoring the high-level information. Thus, their performance may degrade due to the conflict between heterogeneous features and the noises existing in each individual view. To overcome this problem, we propose a novel Multi-View Ensemble Clustering (MVEC) framework to solve MVC in an Ensemble Clustering (EC) way, which generates Basic Partitions (BPs) for each view individually and seeks for a consensus partition among all the BPs. By this means, we naturally leverage the complementary information of multi-view data in the same partition space. Instead of directly fusing BPs, we employ the low-rank and sparse decomposition to explicitly consider the connection between different views and detect the noises in each view. Moreover, the spectral ensemble clustering task is also involved by our framework with a carefully designed constraint, making MVEC a unified optimization framework to achieve the final consensus partition. Experimental results on six real-world datasets show the efficacy of our approach compared with both MVC and EC methods.

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