Correlation Clustering Methods Research Articles

Discovering Strong Communities in Social Networks Through Chromatic Correlation Clustering

Background: Complex systems involved in biochemistry, neuroscience, physics, engineering and social science are primarily studied and modeled through network structures. The connectivity patterns within these interaction networks are discovered through clustering-like techniques. Community discovery is a related problem to find patterns in networks. Objective: Existing algorithms either try to find few large communities in networks; or try to partition network into small strongly connected communities; that too is time consuming and parameterdependant. Methods/Results: This paper proposes a chromatic correlation clustering method to discover small strong communities in an interaction network in heuristic manner to have low time complexity and a parameter free method. Comparison with other methods over synthetic data is done. Conclusion: Interaction networks are very large, sparse containing few small dense communities that can be discovered only through method specifically designed for the purpose.

Research on Grey Relational Clustering Model of Multiobjective Human Resources Based on Time Constraint

The existing grey relational clustering method has limitations in the application of multidimensional sequences and cannot directly calculate the grey correlation degree between unequal-length sequences. In this paper, by introducing the multidimensional dynamic DTW distance into the existing 3D grey relational model, a new grey relational analysis model that can be applied to multidimensional data is proposed, which is based on DTW distance. The model does not require one-to-one correspondence of data points but evaluates the similarity of its geometric curves by calculating the shortest distance between sequences. In addition, since the traditional grey correlation clustering method is implemented, the method first extracts the reference sequence from the observation sequence and then calculates the similarity between the observation objects by calculating the grey correlation degree between each sequence and the reference sequence, so each object only needs to be calculated once. The experimental results show that the multidimensional grey correlation degree based on DTW distance and the grey relational clustering model oriented to multidimensional data are more accurate than other existing methods. Finally, the grey relation clustering method of multidimensional data is used to analyze the multiobjective human resource grey relational clustering model under time constraints, and the validity of the model is verified.

Star-based learning correlation clustering

Correlation clustering (CC) is a clustering method using a signed graph as input without specifying the number of clusters a priori. It had been widely used in real applications, such as social network and text mining. However, its exact optimization or approximate algorithms often give unsatisfactory results, especially for large-scale signed graphs. This paper tackles this problem and proposes a novel CC algorithm, termed star-based learning correlation clustering (SL-CC). The proposed SL-CC contains two phases. The first is a scale reduction for signed graphs. We propose a special motif, called a star structure, for reducing the scale of signed graphs. We assign the vertices within a star structure to have the same cluster label and then merge these vertices as a new vertex in the graph so we can shrink a large-scale graph to a much small-scale one. The second is to give a learning schema for the local search on the reduced graphs. It can discover some important stars as seeds of clusters according to the graph structure, and then justify whether the other stars need to be merged with seeds or not. We also construct a new integer linear programing (ILP) model based on cycle inequalities to perform the local search with final clustering results. The experiments and comparisons of the proposed SL-CC with some existing CC methods on synthetic and real data sets with variant scale structures of signed graphs demonstrate the efficiency and usefulness of the SL-CC algorithm.

An Unsupervised Learning Short Text Clustering Method

Due to the continuous development of Natural Language Processing (NLP), the task of short text categorization has been paid more and more attention. In short text clustering, the high-dimensional sparseness of text representation matrix becomes a challenging problem. This paper proposes a deep embedded method for feature extraction and clustering allocation using auto encoder of sentence distributed embedding. This method maps from data space to low-dimensional feature space and iteratively optimizes clustering targets. Experimental results on three short Chinese text data sets verify the effectiveness of the method. Moreover, it is superior to the existing correlation clustering methods.

Detecting global hyperparaboloid correlated clusters: a Hough-transform based multicore algorithm

Correlation clustering detects complex and intricate relationships in high-dimensional data by identifying groups of data points, each characterized by differents correlation among a (sub)set of features. Current correlation clustering methods generally limit themselves to linear correlations only. In this paper, we introduce a method for detecting global non-linear correlated clusters focusing on quadratic relations. We introduce a novel Hough transform for the detection of hyperparaboloids and apply it to the detection of hyperparaboloid correlated clusters in arbitrary high-dimensional data spaces. We further provide a solution for utilizing all available CPU cores on a system. For this we simply split the Hough space among a pre-defined axis into a number of equi-sized partitions. In this paper we show that this most simple way of parallelization already improves the runtime significantly. Non-linear correlation clustering like our method can reveal valuable insights which are not covered by current linear versions. Our empirical results on synthetic and real world data reveal that the proposed method is robust against noise, jitter and irregular densities.

A new correlation clustering method for cancer mutation analysis

Cancer genomes exhibit a large number of different alterations that affect many genes in a diverse manner. An improved understanding of the generative mechanisms behind the mutation rules and their influence on gene community behavior is of great importance for the study of cancer. To expand our capability to analyze combinatorial patterns of cancer alterations, we developed a rigorous methodology for cancer mutation pattern discovery based on a new, constrained form of correlation clustering. Our new algorithm, named C3 (Cancer Correlation Clustering), leverages mutual exclusivity of mutations, patient coverage and driver network concentration principles. To test C3, we performed a detailed analysis on TCGA breast cancer and glioblastoma data and showed that our algorithm outperforms the state-of-the-art CoMEt method in terms of discovering mutually exclusive gene modules and identifying biologically relevant driver genes. The proposed agnostic clustering method represents a unique tool for efficient and reliable identification of mutation patterns and driver pathways in large-scale cancer genomics studies, and it may also be used for other clustering problems on biological graphs. The source code for the C3 method can be found at https://github.com/jackhou2/C3 CONTACTS: jianma@cs.cmu.edu or milenkov@illinois.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Two-Tiered Correlation Clustering Method for Entity Resolution in Big Data

Two-Tiered Correlation Clustering Method for Entity Resolution in Big Data

Finding multiple global linear correlations in sparse and noisy data sets

Finding linear correlations is an important research problem with numerous real-world applications. In real-world data sets, linear correlation may not exist in the entire data set. Some linear correlations are only visible in certain data subsets. On one hand, a lot of local correlation clustering algorithms assume that the data points of a linear correlation are locally dense. These methods may miss some global correlations when data points are sparsely distributed. On the other hand, existing global correlation clustering methods may fail when the data set contains a large amount of non-correlated points or the actual correlations are coarse. This paper proposes a simple and fast algorithm DCSearch for finding multiple global linear correlations in a data set. This algorithm is able to find the coarse and global linear correlation in noisy and sparse data sets. By using the classical divide and conquer strategy, it first divides the data set into subsets to reduce the search space, and then recursively searches and prunes the candidate correlations from the subsets. Empirical studies show that DCSearch can efficiently reduce the number of candidate correlations during each iteration. Experimental results on both synthetic and real data sets demonstrate that DCSearch is effective and efficient in finding global linear correlations in sparse and noisy data sets.