scholarly journals A Fuzzy Co-Clustering Algorithm via Modularity Maximization

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Yongli Liu ◽  
Jingli Chen ◽  
Hao Chao
Keyword(s):  
Optimization Problem ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Experimental Studies ◽  
Optimization Procedure ◽  
Block Diagonal Matrix ◽  
Benchmark Datasets ◽  
Modularity Maximization ◽  
Object Feature ◽  
Block Diagonal

In this paper we propose a fuzzy co-clustering algorithm via modularity maximization, named MMFCC. In its objective function, we use the modularity measure as the criterion for co-clustering object-feature matrices. After converting into a constrained optimization problem, it is solved by an iterative alternative optimization procedure via modularity maximization. This algorithm offers some advantages such as directly producing a block diagonal matrix and interpretable description of resulting co-clusters, automatically determining the appropriate number of final co-clusters. The experimental studies on several benchmark datasets demonstrate that this algorithm can yield higher quality co-clusters than such competitors as some fuzzy co-clustering algorithms and crisp block-diagonal co-clustering algorithms, in terms of accuracy.

scholarly journals Multi-view Clustering via Late Fusion Alignment Maximization

2019 ◽  
Author(s):  
Siwei Wang ◽  
Xinwang Liu ◽  
En Zhu ◽  
Chang Tang ◽  
Jiyuan Liu ◽  
...  
Keyword(s):  
Computational Complexity ◽  
Iterative Algorithm ◽  
Optimization Problem ◽  
Optimization Procedure ◽  
Multiple Views ◽  
Late Fusion ◽  
Complementary Information ◽  
Benchmark Datasets ◽  
Effectiveness And Efficiency ◽  
Consensus Partition

Multi-view clustering (MVC) optimally integrates complementary information from different views to improve clustering performance. Although demonstrating promising performance in many applications, we observe that most of existing methods directly combine multiple views to learn an optimal similarity for clustering. These methods would cause intensive computational complexity and over-complicated optimization. In this paper, we theoretically uncover the connection between existing k-means clustering and the alignment between base partitions and consensus partition. Based on this observation, we propose a simple but effective multi-view algorithm termed {Multi-view Clustering via Late Fusion Alignment Maximization (MVC-LFA)}. In specific, MVC-LFA proposes to maximally align the consensus partition with the weighted base partitions. Such a criterion is beneficial to significantly reduce the computational complexity and simplify the optimization procedure. Furthermore, we design a three-step iterative algorithm to solve the new resultant optimization problem with theoretically guaranteed convergence. Extensive experiments on five multi-view benchmark datasets demonstrate the effectiveness and efficiency of the proposed MVC-LFA.

Non-convex clustering via proximal alternating linearized minimization method

2018 ◽  
Vol 16 (02) ◽  
pp. 1840013
Author(s):  
Ting Xie ◽  
Feiyu Chen
Keyword(s):  
Optimization Problem ◽  
Clustering Algorithm ◽  
Learning Task ◽  
Convex Constraints ◽  
Minimization Method ◽  
Clustering Problem ◽  
Research Fields ◽  
Wide Range ◽  
Benchmark Datasets ◽  
Convex Clustering

Clustering is a fundamental learning task in a wide range of research fields. The most popular clustering algorithm is arguably the K-means algorithm, it is well known that the performance of K-means algorithm heavily depends on initialization due to its strong non-convexity nature. To overcome the initialization issue, in this paper, we first relax the K-means model as an optimization problem with non-convex constraints, then employ the Proximal Alternating Linearized Minimization (PALM) method to solve the relaxed non-convex optimization model. The convergence analysis of PALM algorithm for the clustering problem is also provided. Experimental results on several benchmark datasets are conducted to evaluate the efficiency of our approach.

scholarly journals Localized Incomplete Multiple Kernel k-means

2018 ◽  
Author(s):  
Xinzhong Zhu ◽  
Xinwang Liu ◽  
Miaomiao Li ◽  
En Zhu ◽  
Li Liu ◽  
...  
Keyword(s):  
Iterative Algorithm ◽  
Local Structure ◽  
Optimization Problem ◽  
Clustering Algorithm ◽  
Recent Literature ◽  
State Of The Art ◽  
Nearest Neighbors ◽  
K Nearest Neighbors ◽  
Multiple Kernel ◽  
Benchmark Datasets

The recently proposed multiple kernel k-means with incomplete kernels (MKKM-IK) optimally integrates a group of pre-specified incomplete kernel matrices to improve clustering performance. Though it demonstrates promising performance in various applications, we observe that it does not \emph{sufficiently  consider the local structure among data and indiscriminately forces all pairwise sample similarity to equally align with their ideal similarity values}. This could make the incomplete kernels less effectively imputed, and in turn adversely affect the clustering performance. In this paper, we propose a novel localized incomplete multiple kernel k-means (LI-MKKM) algorithm to address this issue. Different from existing MKKM-IK, LI-MKKM only requires the similarity of a sample to its k-nearest neighbors to align with their ideal similarity values. This helps the clustering algorithm to focus on closer sample pairs that shall stay together and avoids involving unreliable similarity evaluation for farther sample pairs. We carefully design a three-step iterative algorithm to solve the resultant optimization problem and theoretically prove its convergence. Comprehensive experiments on eight benchmark datasets demonstrate that our algorithm significantly outperforms the state-of-the-art comparable algorithms proposed in the recent literature, verifying the advantage of considering local structure.

Exploring performance of clustering methods on document sentiment analysis

2016 ◽  
Vol 43 (1) ◽  
pp. 54-74 ◽  
Author(s):  
Baojun Ma ◽  
Hua Yuan ◽  
Ye Wu
Keyword(s):  
Sentiment Analysis ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Experimental Studies ◽  
Experimental Results ◽  
Clustering Methods ◽  
Term Weighting ◽  
Weighting Method ◽  
Clustering Techniques ◽  
Better Than

Clustering is a powerful unsupervised tool for sentiment analysis from text. However, the clustering results may be affected by any step of the clustering process, such as data pre-processing strategy, term weighting method in Vector Space Model and clustering algorithm. This paper presents the results of an experimental study of some common clustering techniques with respect to the task of sentiment analysis. Different from previous studies, in particular, we investigate the combination effects of these factors with a series of comprehensive experimental studies. The experimental results indicate that, first, the K-means-type clustering algorithms show clear advantages on balanced review datasets, while performing rather poorly on unbalanced datasets by considering clustering accuracy. Second, the comparatively newly designed weighting models are better than the traditional weighting models for sentiment clustering on both balanced and unbalanced datasets. Furthermore, adjective and adverb words extraction strategy can offer obvious improvements on clustering performance, while strategies of adopting stemming and stopword removal will bring negative influences on sentiment clustering. The experimental results would be valuable for both the study and usage of clustering methods in online review sentiment analysis.

Bayesian hierarchical K-means clustering

2020 ◽  
Vol 24 (5) ◽  
pp. 977-992
Author(s):  
Yue Liu ◽  
Bufang Li
Keyword(s):  
Probability Distribution ◽  
Objective Function ◽  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Expectation Maximization Algorithm ◽  
Synthetic Data ◽  
Cluster Tree ◽  
Bayesian Hierarchical ◽  
Benchmark Datasets

Clustering algorithm is the foundation and important technology in data mining. In fact, in the real world, the data itself often has a hierarchical structure. Hierarchical clustering aims at constructing a cluster tree, which reveals the underlying modal structure of a complex density. Due to its inherent complexity, most existing hierarchical clustering algorithms are usually designed heuristically without an explicit objective function, which limits its utilization and analysis. K-means clustering, the well-known simple yet effective algorithm which can be expressed from the view of probability distribution, has inherent connection to Mixture of Gaussians (MoG). At this point, we consider combining Bayesian theory analysis with K-means algorithm. This motivates us to develop a hierarchical clustering based on K-means under the probability distribution framework, which is different from existing hierarchical K-means algorithms processing data in a single-pass manner along with heuristic strategies. For this goal, we propose an explicit objective function for hierarchical clustering, termed as Bayesian hierarchical K-means (BHK-means). In our method, a cascaded clustering tree is constructed, in which all layers interact with each other in the network-like manner. In this cluster tree, the clustering results of each layer are influenced by the parent and child nodes. Therefore, the clustering result of each layer is dynamically improved in accordance with the global hierarchical clustering objective function. The objective function is solved using the same algorithm as K-means, the Expectation-maximization algorithm. The experimental results on both synthetic data and benchmark datasets demonstrate the effectiveness of our algorithm over the existing related ones.

scholarly journals Combinatorial Optimization-Based Clustering Algorithm for Wireless Sensor Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yuxiao Cao ◽  
Zhen Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Clustering Algorithm ◽  
Fitness Function ◽  
Clustering Algorithms ◽  
Wireless Sensor ◽  
Binary Particle Swarm Optimization ◽  
Conflicting Objectives

As node energy of wireless sensor networks (WSN) is limited and cannot be supplemented after exhaustion, clustering algorithm is frequently taken as an effective method to prolong the lifetime of WSN. However, the existing clustering algorithms have some drawbacks, either consuming excessive energy as a result of exchanging too much controlling information between nodes, or lacking a comprehensive perspective in terms of the balance among several conflicting objectives. In order to overcome these shortcomings, a novel combinatorial optimization-based clustering algorithm (COCA) for WSN is proposed in this paper. Different from the above mentioned algorithms which take clustering as a continuous optimization problem, COCA solves the clustering problem from the perspective of combinatorial optimization. Firstly, the clustering of WSN is abstracted into a combinatorial optimization problem. Then, the binary particle coding scheme of cluster head is proposed, which is based on the corresponding relationship between nodes and particle position vectors, and the fitness function is designed according to the parameters used in the process of cluster formation. Finally, the binary particle swarm optimization algorithm is applied to implement the clustering. COCA is validated under different scenarios compared with three other clustering algorithms. The simulation results show that COCA has better performance than its comparable algorithms.

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

DRSA: a non-hierarchical clustering algorithm using k-NN graph and its application in vegetation classification

2015 ◽  
pp. 125-138 ◽  
Author(s):  
I. V. Goncharenko
Keyword(s):  
Cluster Analysis ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Clustering Algorithms ◽  
Protein Structures ◽  
Hierarchical Cluster ◽  
Vegetation Classification ◽  
K Nearest Neighbor ◽  
Neighbor Graph ◽  
Nearest Neighbor Graph

In this article we proposed a new method of non-hierarchical cluster analysis using k-nearest-neighbor graph and discussed it with respect to vegetation classification. The method of k-nearest neighbor (k-NN) classification was originally developed in 1951 (Fix, Hodges, 1951). Later a term “k-NN graph” and a few algorithms of k-NN clustering appeared (Cover, Hart, 1967; Brito et al., 1997). In biology k-NN is used in analysis of protein structures and genome sequences. Most of k-NN clustering algorithms build «excessive» graph firstly, so called hypergraph, and then truncate it to subgraphs, just partitioning and coarsening hypergraph. We developed other strategy, the “upward” clustering in forming (assembling consequentially) one cluster after the other. Until today graph-based cluster analysis has not been considered concerning classification of vegetation datasets.

User Power Behavior Similarity Clustering Based on Unsupervised Extreme Learning Machine Algorithm

2020 ◽  
Vol 13 (5) ◽  
pp. 641-649
Author(s):  
Yuancheng Li ◽  
Yaqi Cui ◽  
Xiaolong Zhang
Keyword(s):  
Extreme Learning Machine ◽  
Clustering Algorithm ◽  
Characteristic Curve ◽  
Clustering Algorithms ◽  
Data Sets ◽  
Residential Areas ◽  
Processing Power ◽  
Learning Machine ◽  
Advanced Metering ◽  
Matlab Programming

Background: Advanced Metering Infrastructure (AMI) for the smart grid is growing rapidly which results in the exponential growth of data collected and transmitted in the device. By clustering this data, it can give the electricity company a better understanding of the personalized and differentiated needs of the user. Objective: The existing clustering algorithms for processing data generally have some problems, such as insufficient data utilization, high computational complexity and low accuracy of behavior recognition. Methods: In order to improve the clustering accuracy, this paper proposes a new clustering method based on the electrical behavior of the user. Starting with the analysis of user load characteristics, the user electricity data samples were constructed. The daily load characteristic curve was extracted through improved extreme learning machine clustering algorithm and effective index criteria. Moreover, clustering analysis was carried out for different users from industrial areas, commercial areas and residential areas. The improved extreme learning machine algorithm, also called Unsupervised Extreme Learning Machine (US-ELM), is an extension and improvement of the original Extreme Learning Machine (ELM), which realizes the unsupervised clustering task on the basis of the original ELM. Results: Four different data sets have been experimented and compared with other commonly used clustering algorithms by MATLAB programming. The experimental results show that the US-ELM algorithm has higher accuracy in processing power data. Conclusion: The unsupervised ELM algorithm can greatly reduce the time consumption and improve the effectiveness of clustering.

Pinball Loss Twin Support Vector Clustering

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-23
Author(s):  
M. Tanveer ◽  
Tarun Gupta ◽  
Miten Shah ◽  
Keyword(s):  
Loss Function ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Structural Mri ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Support Vector Clustering ◽  
Hinge Loss ◽  
Pinball Loss ◽  
Vector Clustering

Twin Support Vector Clustering (TWSVC) is a clustering algorithm inspired by the principles of Twin Support Vector Machine (TWSVM). TWSVC has already outperformed other traditional plane based clustering algorithms. However, TWSVC uses hinge loss, which maximizes shortest distance between clusters and hence suffers from noise-sensitivity and low re-sampling stability. In this article, we propose Pinball loss Twin Support Vector Clustering (pinTSVC) as a clustering algorithm. The proposed pinTSVC model incorporates the pinball loss function in the plane clustering formulation. Pinball loss function introduces favorable properties such as noise-insensitivity and re-sampling stability. The time complexity of the proposed pinTSVC remains equivalent to that of TWSVC. Extensive numerical experiments on noise-corrupted benchmark UCI and artificial datasets have been provided. Results of the proposed pinTSVC model are compared with TWSVC, Twin Bounded Support Vector Clustering (TBSVC) and Fuzzy c-means clustering (FCM). Detailed and exhaustive comparisons demonstrate the better performance and generalization of the proposed pinTSVC for noise-corrupted datasets. Further experiments and analysis on the performance of the above-mentioned clustering algorithms on structural MRI (sMRI) images taken from the ADNI database, face clustering, and facial expression clustering have been done to demonstrate the effectiveness and feasibility of the proposed pinTSVC model.

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