Divisive Hierarchical Clustering Algorithm Research Articles

An Automatic Hierarchical Clustering Method for the LiDAR Point Cloud Segmentation of Buildings via Shape Classification and Outliers Reassignment

Shape segmentation in urban environments forms the foundation for tasks such as classification and reconstruction. Most artificial buildings with complex structures are composed of multiple simple geometric primitives. Based on this assumption, this paper proposes a divisive hierarchical clustering algorithm that uses shape classification and outliers reassignment to segment LiDAR point clouds in order to effectively identify the various shapes of structures that make up buildings. The proposed method adopts a coarse-to-fine strategy. Firstly, based on the geometric properties of different primitives in a Gaussian sphere space, coarse extraction is performed using Gaussian mapping and the DBSCAN algorithm to identify the primary structure of various shapes. Then, the error functions are constructed after parameterizing the recognized shapes. Finally, a minimum energy loss function is built by combining the error functions and binary integer programming (BIP) to redistribute the outlier points. Thereby, the accurate extraction of geometric primitives is achieved. Experimental evaluations on real point cloud datasets show that the indicators of precision, accuracy, and F1 score of our method are 0.98, 0.95, and 0.96 (point assignment) and 0.97, 0.95, and 0.95 (shape recognition), respectively. Compared with other state-of-the-art methods, the proposed method can efficiently segment planar and non-planar structures with higher quality from building point clouds.

Network-Informed Constrained Divisive Pooled Testing Assignments.

Frequent universal testing in a finite population is an effective approach to preventing large infectious disease outbreaks. Yet when the target group has many constituents, this strategy can be cost prohibitive. One approach to alleviate the resource burden is to group multiple individual tests into one unit in order to determine if further tests at the individual level are necessary. This approach, referred to as a group testing or pooled testing, has received much attention in finding the minimum cost pooling strategy. Existing approaches, however, assume either independence or very simple dependence structures between individuals. This assumption ignores the fact that in the context of infectious diseases there is an underlying transmission network that connects individuals. We develop a constrained divisive hierarchical clustering algorithm that assigns individuals to pools based on the contact patterns between individuals. In a simulation study based on real networks, we show the benefits of using our proposed approach compared to random assignments even when the network is imperfectly measured and there is a high degree of missingness in the data.

Cognitive Complexity Beyond Generalization: A Subjective Rating for the Human Comprehension

The cognitive complexity of a software determines the comprehension effort of a particular individual faces when designing, developing and maintaining a software. The comprehension level tends to be varied with each human resulting the cognitive complexity a subjective measurement. Expressing the cognitive complexity as a form of metric quantifies the comprehensibility as a generic value, which does not imply the subjectivity of human factor. This study elaborates the significance of expressing the cognitive complexity as a form of a subjective rating. The cognitive complexity rating has been pioneered with respect to the human and programming dependent factors related to human cognition. The Divisive hierarchical clustering algorithm has been used to train and predict the cognition rating per user. It has been clearly elaborated the subjectivity of the cognitive ratings over the quantitative and static complexity values of current cognitive and software complexity metrics. Thereby, the concept of cognition rates has been proposed as a preliminary step of determining and expressing the cognitive complexity.

Adaptive Resonance Theory-Based Topological Clustering With a Divisive Hierarchical Structure Capable of Continual Learning

Adaptive Resonance Theory (ART) is considered as an effective approach for realizing continual learning thanks to its ability to handle the plasticity-stability dilemma. In general, however, the clustering performance of ART-based algorithms strongly depends on the specification of a similarity threshold, i.e., a vigilance parameter, which is data-dependent and specified by hand. This paper proposes an ART-based topological clustering algorithm with a mechanism that automatically estimates a similarity threshold from the distribution of data points. In addition, for improving information extraction performance, a divisive hierarchical clustering algorithm capable of continual learning is proposed by introducing a hierarchical structure to the proposed algorithm. Experimental results demonstrate that the proposed algorithm has high clustering performance comparable with recently-proposed state-of-the-art hierarchical clustering algorithms.

Finding specific peaks (markers) using fuzzy divisive hierarchical associative-clustering based on the chromatographic profiles of medicinal plant extracts obtained at various detection wavelengths.

Advanced chemometric methods, such as fuzzy c-means (FCM), a fuzzy divisive hierarchical clustering algorithm (FDHC), and fuzzy divisive hierarchical associative-clustering (FDHAC), which offer the excellent possibility to associate each fuzzy partition of samples with a fuzzy set of characteristics (features), have been successfully applied in this study. FDHAC, a method that utilizes specific regions of chromatographic fingerprints or specific peaks as a fuzzy set of characteristics, was effectively applied to the characterization and classification of medicinal plant extracts according to their antioxidant capacities, using their chromatographic profiles monitored at 242, 260, 280, 320, 340, and 380 nm via HPLC with a multistep isocratic and gradient elution system and diode array detection (HPLC-DAD). What is quite new is the partitioning of the chromatographic retention time ranges and peaks (markers) and their association with different plant extract samples with high, moderate or low antioxidant capacity. Furthermore, the degrees of membership of fingerprints (fuzzy markers) are highly relevant with respect to the (dis)similarity of samples because they indicate both the positions and degrees of association of chromatographic peaks from different classes or individual samples. The obtained results clearly demonstrate the efficiency and information power of these advanced fuzzy methods for medicinal plant characterization and authentication, and this study generates the premise for a new chemometrics approach with high-impact for use in analytical chemistry and other fields.

Automatic feature group combination selection method based on GA for the functional regions clustering in DBS

Background and ObjectiveThe functional regions clustering through microelectrode recording (MER) is a critical step in deep brain stimulation (DBS) surgery. The localization of the optimal target highly relies on the neurosurgeon's empirical assessment of the neurophysiological signal. This work presents an unsupervised clustering algorithm to get the optimal cluster result of the functional regions along the electrode trajectory. MethodsThe dataset consists of the MERs obtained from the routine bilateral DBS for PD patients. Several features have been extracted from MER and divided into groups based on the type of neurophysiological signal. We selected single feature groups rather than all features as the input samples of each division of the divisive hierarchical clustering (DHC) algorithm. And the optimal cluster result has been achieved through a feature group combination selection (FGS) method based on genetic algorithm (GA). To measure the performance of this method, we compared the accuracy and validation indexes of three methods, including DHC only, DHC with GA-based FGS and DHC with GA-based feature selection (FS) in other studies, on the universal and DBS datasets. ResultsResults show that the DHC with GA-based FGS achieved the optimal cluster result compared with other methods. The three borders of the STN can be identified from the cluster result. The dorsoventral sizes of the STN and dorsal STN are 4.45 mm and 2.02 mm. In addition, the features extracted from the multiunit activity, background unit activity and local field potential are found to be the most representative feature groups to identify the dorsal, d-v and ventral borders of the STN, respectively. ConclusionsOur clustering algorithm showed a reliable performance of the automatic identification of functional regions in DBS. The findings can provide valuable assistance for both neurosurgeons and stereotactic surgical robots in DBS surgery.

Robust spike sorting of retinal ganglion cells tuned to spot stimuli.

We propose an automatic spike sorting approach for the data recorded from a microelectrode array during visual stimulation of wild type retinas with tiled spot stimuli. The approach first detects individual spikes per electrode by their signature local minima. With the mixture probability distribution of the local minima estimated afterwards, it applies a minimum-squared-error clustering algorithm to sort the spikes into different clusters. A template waveform for each cluster per electrode is defined, and a number of reliability tests are performed on it and its corresponding spikes. Finally, a divisive hierarchical clustering algorithm is used to deal with the correlated templates per cluster type across all the electrodes. According to the measures of performance of the spike sorting approach, it is robust even in the cases of recordings with low signal-to-noise ratio.

Finding common task-related regions in fMRI data from multiple subjects by periodogram clustering and clustering ensemble.

We propose an innovative and practically relevant clustering method to find common task-related brain regions among different subjects who respond to the same set of stimuli. Using functional magnetic resonance imaging (fMRI) time series data, we first cluster the voxels within each subject on a voxel by voxel basis. To extract signals out of noisy data, we estimate a new periodogram at each voxel using multi-tapering and low-rank spline smoothing and then use the periodogram as the main feature for clustering. We apply a divisive hierarchical clustering algorithm to the estimated periodograms within a single subject and identify the task-related region as the cluster of voxels that have periodograms with a peak frequency matching that of the stimulus sequence. Finally, we apply a machine learning technique called clustering ensemble to find common task-related regions across different subjects. The efficacy of the proposed approach is illustrated via a simulation study and a real fMRI data set. Copyright © 2016 John Wiley & Sons, Ltd.

Parallel community detection on large graphs with MapReduce and GraphChi

Community detection from social network data gains much attention from academia and industry since it has many real-world applications. The Girvan–Newman (GN) algorithm is a divisive hierarchical clustering algorithm for community detection, which is regarded as one of the most popular algorithms. It exploits the concept of edge betweenness to divide a network into multiple communities. Though it is being widely used, it has limitations in supporting large-scale networks since it needs to calculate the shortest path between every pair of vertices in a network. In this paper, we develop two parallel versions of the GN algorithm to support large-scale networks. First, we propose a new algorithm, which we call Shortest Path Betweenness MapReduce Algorithm (SPB-MRA), that utilizes the MapReduce model. Second, we propose another new algorithm, which we call Shortest Path Betweenness Vertex-Centric Algorithm (SPB-VCA), that utilizes the vertex-centric model. An approximation technique is also developed to further speed up community detection processes. We implemented SPB-MRA using Hadoop and SPB-VCA using GraphChi, and then evaluated the performance of SPB-MRA on Amazon EC2 instances and that of SPB-VCA on a single commodity PC. The evaluation results showed that the elapsed time of SPB-MRA decreased almost linearly as the number of reducers increased, SPB-VCA outperformed SPB-MRA just on a single PC by 4–6 times, and the approximation technique introduced negligible errors.

Chronic T2 Lesions in Multiple Sclerosis are Heterogeneous Regarding Phase MR Imaging.

Phase imaging provides additional information on multiple sclerosis (MS) lesions and may in combination with mean diffusivity (MD) and magnetization transfer ratio (MTR) help differentiating heterogeneity of MS lesion pathology. Magnetic resonance imaging (MRI) was performed in 23 MS patients including diffusion tensor imaging (DTI), magnetization transfer imaging (MTI), and SWI. Mean values (MTR, MD, and homodyne filtered phase) from 138 chronic MS lesions and normal appearing white matter (NAWM) were obtained and correlations examined. For explorative analysis, a divisive hierarchical clustering algorithm was applied. Phase characteristics were an independent characteristic of chronic T2 lesions, as MTR and MD were not correlated with phase values (R = - 0.23, R = - 0.18). Dependent on MTR, MD, and phase, cluster analysis led to five lesion groups. Of the two groups with phase values close to NAWM, one presented with highest MD and most severe MTR decrease (p = 0.01), the other with slight MD increase and MTR decrease. Two lesion groups with highest phase values (p = 0.01) displayed slightly increased MD and moderate decrease in MTR. Clinical data including EDSS, disease duration, and age did not differ significantly between groups. Increased phase is predominantly detectable in lesions with clear MTR decrease but only moderate MD increase. Phase images seem to represent an independent parameter for MS lesion characterization and may provide additional information on MS lesion heterogeneity.

A Novel Variable-order Markov Model for Clustering Categorical Sequences

Clustering categorical sequences is an important and difficult data mining task. Despite recent efforts, the challenge remains, due to the lack of an inherently meaningful measure of pairwise similarity. In this paper, we propose a novel variable-order Markov framework, named weighted conditional probability distribution (WCPD), to model clusters of categorical sequences. We propose an efficient and effective approach to solve the challenging problem of model initialization. To initialize the WCPD model, we propose to use a first-order Markov model built on a weighted fuzzy indicator vector representation of categorical sequences, which we call the WFI Markov model. Based on a cascade optimization framework that combines the WCPD and WFI models, we design a new divisive hierarchical clustering algorithm for clustering categorical sequences. Experimental results on data sets from three different domains demonstrate the promising performance of our models and clustering algorithm.

Aircraft grouping based on improved divisive hierarchical clustering algorithm

Because air traffic control efficiency can be largely increased by means of group, the aircraft grouping problem was studied based on improved divisive hierarchical clustering algorithm (DHCA). The concepts of position similarity, velocity similarity and heading similarity were defined and the merging rule about position matrix, velocity matrix and heading matrix was put forward. According to the definition of the most similar point and the neighbor set, the end condition of DHCA was brought forward. In order to increase the search efficiency, the neighbor set was saved by the open hash table. The flow of the improved DHCA was set up by binary tree and open hash table. The simulation results show that the improved DHCA can finish aircraft grouping, and moreover, the time complexity analysis proves that the improved DHCA has a better time efficiency than the bisection method.

Social Network Analysis To Detect Inherent Communities based On Constraints

Social network analysis (SNA) is used to analyze social networks or structures made up individuals called nodes, which are tied by one or more specific types of interdependency such as relatio nships, connections, or interactions. Often it is used in many internet-based applications like, social networking websites, on-line viral marketing, and recommendation network based applications to improve the performance of user-specific information dissemination. Detecting communities, which are basically sub-graphs or clusters, within a social network has been the central focus of this work. Here, we present a divisive hierarchical clustering algorithm for detecting disjoint communities by removing minimum number of edges to obey minimum edge-cut principle, like CHAMELEON: Two Phase Agglomerative Hierarchical Clustering. The stopping criteria of this algorithm depends on two threshold constraints namely, balance constraint (BC) and MINSIZE (MS) like CHAMELEON. As a measure of the quality of community, we follow network centrality measure clustering coefficient. Our experimental results, usin g some well-known benchmark social networks, also show that our method determines similar communities with good average clustering coefficient as the other existing well known methods of various research papers.

Closed walks for community detection

In this paper, we propose a novel measure that integrates both the concept of closed walks and clustering coefficients to replace the edge betweenness in the well-known divisive hierarchical clustering algorithm, the Girvan and Newman method (GN). The edges with the lowest value are removed iteratively until the network is degenerated into isolated nodes. The experimental results on computer generated networks and real-world networks showed that our method makes a better tradeoff of accuracy and runtime. Based on the analysis of the results, we observe that the nontrivial closed walks of order three and four can be considered as the basic elements in constructing community structures. Meanwhile, we discover that those nontrivial closed walks outperform trivial closed walks in the task of analyzing the structure of networks. The double peak structure problem is mentioned in the last part of the article. We find that our proposed method is a novel way to solve the double peak structure problem. Our work can provide us with a new perspective for understanding community structure in complex networks.

Building a Post-Search Academic Search Engine Based on a Serial of Clustering Methods

Academic search engines, such as Google Scholar and Scirus, provide a Web-based interface to effectively find relevant scientific articles to researchers. However, current academic search engines are lacking the ability to cluster the search results into a hierarchical tree structure. In this paper, we develop a post-search academic search engine by using a mixed clustering method. In this method, we first adopt a suffix tree clustering and a two-way hash mechanism to generate all meaningful labels. We then develop a divisive hierarchical clustering algorithm to organize the labels into a hierarchical tree. According to the results of experiments, we conclude that using our mixed clustering method to cluster the search results can give significant performance gains than current academic search engines. In this paper, we make two contributions. First, we present a high performance academic search engine based on our mixed clustering method. Second, we develop a divisive hierarchical clustering algorithm to organize all returned search results into a hierarchical tree structure.

Building a web‐snippet clustering system based on a mixed clustering method

Purpose – Web‐snippet clustering has recently attracted a lot of attention as a means to provide users with a succinct overview of relevant results compared with traditional search results. This paper seeks to research the building of a web‐snippet clustering system, based on a mixed clustering method.Design/methodology/approach – This paper proposes a mixed clustering method to organise all returned snippets into a hierarchical tree. The method accomplishes two main tasks: one is to construct the cluster labels and the other is to build a hierarchical tree.Findings – Five measures were used to measure the quality of clustering results. Based on the results of the experiments, it was concluded that the performance of the system is better than current commercial and academic systems.Originality/value – A high performance system is presented, based on the clustering method. A divisive hierarchical clustering algorithm is also developed to organise all returned snippets into a hierarchical tree.

DHCC: Divisive hierarchical clustering of categorical data

Clustering categorical data poses two challenges defining an inherently meaningful similarity measure, and effectively dealing with clusters which are often embedded in different subspaces. In this paper, we propose a novel divisive hierarchical clustering algorithm for categorical data, named DHCC. We view the task of clustering categorical data from an optimization perspective, and propose effective procedures to initialize and refine the splitting of clusters. The initialization of the splitting is based on multiple correspondence analysis (MCA). We also devise a strategy for deciding when to terminate the splitting process. The proposed algorithm has five merits. First, due to its hierarchical nature, our algorithm yields a dendrogram representing nested groupings of patterns and similarity levels at different granularities. Second, it is parameter-free, fully automatic and, in particular, requires no assumption regarding the number of clusters. Third, it is independent of the order in which the data is processed. Fourth, it is scalable to large data sets. And finally, our algorithm is capable of seamlessly discovering clusters embedded in subspaces, thanks to its use of a novel data representation and Chi-square dissimilarity measures. Experiments on both synthetic and real data demonstrate the superior performance of our algorithm.

Detection of Gaseous Plumes in IR Hyperspectral Images—Performance Analysis

The emergence of IR hyperspectral sensing technology in recent years has enabled its use in remote environmental monitoring of gaseous plumes. IR hyperspectral imaging, which combines the unique advantages of traditional remote sensing methods such as multispectral imagery and nonimaging Fourier transform IR, provides significant advantages. The most significant improvement introduced by hyperspectral technology is the capability of standoff detection and discrimination of effluent gaseous plumes without need for a clear reference background, or any other temporal information. In this paper, we introduce a novel approach aimed for detection and identification of gaseous plumes in IR hyperspectral imagery, using a divisive hierarchical clustering algorithm. The utility of the suggested detection algorithm is demonstrated on actual IR hyperspectral images of the release of several atmospheric tracers. The performance analysis of the proposed algorithm and its detection thresholds is presented. The technique of hyperspectral IR imagery can be used for other applications, such as mapping of the propagation of atmospheric tracers in confined spaces as well as in cases of very low winds.

Detection of gaseous plumes in IR hyperspectral images using hierarchical clustering

The emergence of IR hyperspectral sensors in recent years enables their use in remote environmental monitoring of gaseous plumes. IR hyperspectral imaging combines the unique advantages of traditional remote sensing methods such as multispectral imagery and nonimaging Fourier transform infrared spectroscopy, while eliminating their drawbacks. The most significant improvement introduced by hyperspectral technology is the capability of standoff detection and discrimination of effluent gaseous plumes without need for a clear reference background or any other temporal information. We introduce a novel approach for detection and discrimination of gaseous plumes in IR hyperspectral imagery using a divisive hierarchical clustering algorithm. The utility of the suggested detection algorithm is demonstrated on IR hyperspectral images of the release of two atmospheric tracers. The application of the proposed detection method on the experimental data has yielded a correct identification of all the releases without any false alarms. These encouraging results show that the presented approach can be used as a basis for a complete identification algorithm for gaseous pollutants in IR hyperspectral imagery without the need for a clear background.

Learning implicit user interest hierarchy for context in personalization

To provide a more robust context for personalization, we desire to extract a continuum of general to specific interests of a user, called a user interest hierarchy (UIH). The higher-level interests are more general, while the lower-level interests are more specific. A UIH can represent a user's interests at different abstraction levels and can be learned from the contents (words/phrases) in a set of web pages bookmarked by a user. We propose a divisive hierarchical clustering (DHC) algorithm to group terms (topics) into a hierarchy where more general interests are represented by a larger set of terms. Our approach does not need user involvement and learns the UIH "implicitly". To enrich features used in the UIH, we used phrases in addition to words. Our experiment indicates that DHC with the Augmented Expected Mutual Information (AEMI) correlation function and MaxChildren threshold-finding method built more meaningful UIHs than the other combinations on average; using words and phrases as features improved the quality of UIHs.