4.1. Feature-based datasets

Audio genre [80] consists of 1886 audio tracks classified into 9 music genres, which are Blues, Electronic, Jazz, Pop, Rap/HipHop, Rock, Folk/Country, Alternative, and Funk/Soul. Fortynine low-level audio features have been extracted and they are grouped into 15 vector spaces.

multi-view clustering can give satisfactory results under this condition. In some cases, classical methods can also give good performance for feature-based datasets where all features are descriptions of the same object from different perspectives. For graph-based datasets, multiview clustering naturally fits into them since different graphs can be processed by different

New Approaches in Multi-View Clustering http://dx.doi.org/10.5772/intechopen.75598 213

For both feature-based and graph-based datasets, when the scale of datasets becomes significantly large, most multi-view clustering methods have the potential to outperform other clustering methods on speed. For example, multi-view matrix factorization is quite suitable to

Although multi-view clustering has demonstrated its superiority over single-view clustering in many applications, there are still many open issues deserving much more attention from

Although there are many typical methods to construct views, they all have their own drawbacks. It is well known that if we cannot extract valuable information from the original data and put it into different views appropriately, the performance will be highly limited no matter how delicate the algorithm is. So it is important to find efficient ways of constructing and

When constructing different views, we may find that for some views, the information is not complete. In other words, even though we know how to construct views appropriately, we do not have enough information to do it, which is very common in practical problems. In real world, it is very difficult to ensure the completeness of data. This unbalanced relationship between complete views and incomplete views could cause huge problems. Moreover, these incomplete views may influence views with complete information. To solve it, one possible

In multi-view learning, sometimes researchers will convert single-view data into multiple views and apply relevant algorithms on them. In practice, it may give good performance, but there are few theoretical researches on the proof of its reliability. Since the original data is single view, it is important to make it clear: is it necessary to complicate a simple task? We should not only focus on the final performance, the trade-off between cost and benefit is also

way is to construct these lost information from other views.

both academia and industry. Several vital open issues are summarized in this part.

views.

parallel process.

5. Open issues

5.1. View construction

evaluating multiple views.

5.3. Single-view to multi-view

important.

5.2. Incomplete view

NUS-WIDE [81] is a web image dataset composed of 269,648 images, 5018 related tags, and 81 ground-truth concepts. Six types of low-level features have been extracted: 64-D color histogram, 144-D color correlogram, 73-D edge direction histogram, 128-D wavelet texture, 225-D block-wise color moments extracted over 55 fixed grid partitions, and 500-D bag of words based on SIFT descriptions.

UCF101 [82] consists of 101 human action classes. These actions can be divided into five types: human-object interaction, body-motion only, human-human interaction, playing musical instruments, and sports. There are over 13,000 clips and 27 hours of video data in it.

Handwritten numerals [83] is composed of 2000 handwritten digits which are divided into 10 classes. Four types of feature sets have been extracted: Zernike moments, Karhunen-Loeve features, Fourier descriptors, and image vectors. For Zernike set, it has 47 rotation invariant Zernike moments and 6 morphological features. For Fourier set, it has 76 two-dimensional shape descriptors. Both Zernike and Fourier feature sets are rotation invariant. For Karhunen-Loeve set, it has 64 Karhunen-Loeve transform which corresponds to the projection of images onto the eigenvectors of a covariance matrix.

#### 4.2. Graph-based datasets

DBLP coauthorship [84] is a coauthorship network composed of 10,305 authors. There are 617 layers in it, each layer representing different publication categories.

Facebook [85] is a three-layer social network composed of 1640 users with multiple types of ties. The first layer shows whether two users are friends. The second layer shows whether users are in a same group. The third layer shows whether users are in the same photos uploaded by users.

CiteSeer [86] consists of 3312 scientific publications classified into 6 classes, which are Agents, AI, DB, IR, ML, and HCI. It can be represented as an annotated network, where nodes represent scientific publications and links represent the citation relationships. For each node, there is a 3703-dimensional one-hot encoding vector representing the absence/presence of key words.

Enron e-mail [87] consists of 184 users and 44 layers. Although it is a temporal network, it can be considered as a multi-layer network. Each layer represents communication in different months.

#### 4.3. Performance on different datasets

For feature-based datasets, when confronted with the situation where we need to reconstruct the views, the performance of classical methods, like deep learning, is not promising. But multi-view clustering can give satisfactory results under this condition. In some cases, classical methods can also give good performance for feature-based datasets where all features are descriptions of the same object from different perspectives. For graph-based datasets, multiview clustering naturally fits into them since different graphs can be processed by different views.

For both feature-based and graph-based datasets, when the scale of datasets becomes significantly large, most multi-view clustering methods have the potential to outperform other clustering methods on speed. For example, multi-view matrix factorization is quite suitable to parallel process.
