Tag Archives: Hierarchical Clustering

A fast method to cluster networks that include both randomness and structure, with a nice summary of existing clustering algorithms

Blondel V.D., Guillaume J.-L., Lambiotte R., Lefebvre E., Fast unfolding of communities in large networks, . Stat. Mech. Theory Exp., 2008 (10) (2008), Article P10008, DOI: 10.1088/1742-5468/2008/10/P10008.

We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection methods in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2 million customers and by analysing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad hoc modular networks.

An interesting soft-partition method based on hierarchical graphs (trees, actually) applied to topic detection in documents

Peixian Chen, Nevin L. Zhang, Tengfei Liu, Leonard K.M. Poon, Zhourong Chen, Farhan Khawar, Latent tree models for hierarchical topic detection, Artificial Intelligence, Volume 250, 2017, Pages 105-124, DOI: 10.1016/j.artint.2017.06.004.

We present a novel method for hierarchical topic detection where topics are obtained by clustering documents in multiple ways. Specifically, we model document collections using a class of graphical models called hierarchical latent tree models (HLTMs). The variables at the bottom level of an HLTM are observed binary variables that represent the presence/absence of words in a document. The variables at other levels are binary latent variables that represent word co-occurrence patterns or co-occurrences of such patterns. Each latent variable gives a soft partition of the documents, and document clusters in the partitions are interpreted as topics. Latent variables at high levels of the hierarchy capture long-range word co-occurrence patterns and hence give thematically more general topics, while those at low levels of the hierarchy capture short-range word co-occurrence patterns and give thematically more specific topics. In comparison with LDA-based methods, a key advantage of the new method is that it represents co-occurrence patterns explicitly using model structures. Extensive empirical results show that the new method significantly outperforms the LDA-based methods in term of model quality and meaningfulness of topics and topic hierarchies.

A new method of clustering of data with many advantages w.r.t. others

A. Sharma, K. A. Boroevich, D. Shigemizu, Y. Kamatani, M. Kubo and T. Tsunoda, “Hierarchical Maximum Likelihood Clustering Approach,” in IEEE Transactions on Biomedical Engineering, vol. 64, no. 1, pp. 112-122, Jan. 2017. DOI: 10.1109/TBME.2016.2542212.

In this paper, we focused on developing a clustering approach for biological data. In many biological analyses, such as multiomics data analysis and genome-wide association studies analysis, it is crucial to find groups of data belonging to subtypes of diseases or tumors. Methods: Conventionally, the k-means clustering algorithm is overwhelmingly applied in many areas including biological sciences. There are, however, several alternative clustering algorithms that can be applied, including support vector clustering. In this paper, taking into consideration the nature of biological data, we propose a maximum likelihood clustering scheme based on a hierarchical framework. Results: This method can perform clustering even when the data belonging to different groups overlap. It can also perform clustering when the number of samples is lower than the data dimensionality. Conclusion: The proposed scheme is free from selecting initial settings to begin the search process. In addition, it does not require the computation of the first and second derivative of likelihood functions, as is required by many other maximum likelihood-based methods. Significance: This algorithm uses distribution and centroid information to cluster a sample and was applied to biological data. A MATLAB implementation of this method can be downloaded from the web link http://www.riken.jp/en/research/labs/ims/med_sci_math/.