Abstract Incomplete Multi-View Clustering (IMVC) attempts to give an optimal clustering solution for incomplete multi-view data that suffer from missing instances in certain views. However, most existing IMVC methods still have various drawbacks practical applications, such as arbitrary scenarios cannot be handled; the computational cost is relatively high; valuable nonlinear relations among sa...

In the past few years, numerous multi-view graph clustering algorithms have been proposed to enhance performance by exploring information from multiple views. Despite superior performance, high time and space expenditures limit their scalability. Accordingly, anchor learning has introduced alleviate computational complexity. However, existing approaches can be further improved following conside...

fuzzy clustering methods are conveniently employed in constructing a fuzzy model of a system, but they need to tune some parameters. in this research, fcm is chosen for fuzzy clustering. parameters such as the number of clusters and the value of fuzzifier significantly influence the extent of generalization of the fuzzy model. these two parameters require tuning to reduce the overfitting in the...

Expert input can improve clustering performance. In today’s collaborative environment, the availability of crowdsourced multiple expert input is becoming common. Given multiple experts’ inputs, most existing approaches can only discover one clustering structure. However, data is multi-faceted by nature and can be clustered in different ways (also known as views). In an exploratory analysis prob...

Hierarchical clustering is widely used to find patterns in multi-dimensional datasets, especially for genomic microarray data. Finding groups of genes with similar expression patterns can lead to better understanding of the functions of genes. Early software tools produced only printed results, while newer ones enabled some online exploration. We describe four general techniques that could be u...

Different approaches have been used for LDI construction in a multi-view context. This article proposes a simple adaptive way to build a Layered Depth Image (LDI) representation and improve the trade-off between the number of layers and the synthesized view quality. The LDI construction scheme is first recalled, followed by different classification algorithms which aim at reducing the inter-lay...

This paper proposes a spatial clustering model based on self-organizing feature map and a composite distance measure, and studies the knowledge discovery from spatial database of spatial objects with non-spatial attributes. This paper gives the structure, algorithm of the spatial clustering model based on SOFM. We put forward a composite clustering statistic, which is calculated by both geograp...

of the Dissertation Spectral Clustering for Complex Settings Many real-world datasets can be modeled as graphs, where each node corresponds to a data instance and an edge represents the relation/similarity between two nodes. To partition the nodes into different clusters, spectral clustering is used to find the normalized minimum cut of the graph (in the relaxed sense). As one of the most popul...