Constrained Clustering Approach Research Articles

Evolution-aware Constraint Derivation Approach for Software Remodularization

Existing software clustering techniques tend to ignore prior knowledge from domain experts, leading to results (suggested big-bang remodularization actions) that can not acceptable to developers. Incorporating domain experts knowledge or constraints during clustering ensures the obtained modularization aligns with developers’ perspectives, enhancing software quality. However, manual review by knowledgeable domain experts for constraint generation is time-consuming and labor-intensive. In this paper, we propose an evolution-aware constraint derivation approach, Escort, which automatically derives clustering constraints based on the evolutionary history from the analyzed software. Specifically, Escort can serve as an alternative approach to derive implicit and explicit constraints in situations where domain experts are absent. In the subsequent constrained clustering process, Escort can be considered as a framework to help supplement and enhance various unconstrained clustering techniques to improve their accuracy and reliability. We evaluate Escort based on both quantitative and qualitative analysis. In quantitative validation, Escort, using generated clustering constraints, outperforms seven classic unconstrained clustering techniques. Qualitatively, a survey with developers from five IT companies indicates that 89% agree with Escort’s clustering constraints. We also evaluate the utility of refactoring suggestions from our constrained clustering approach, with 54% acknowledged by project developers, either implemented or planned for future releases.

Correction to: Multispectral image segmentation utilizing constrained clustering approach and CGT classifier

Correction to: Multispectral image segmentation utilizing constrained clustering approach and CGT classifier

Multispectral image segmentation utilizing constrained clustering approach and CGT classifier

Multispectral image segmentation utilizing constrained clustering approach and CGT classifier

An adaptive constrained clustering approach for real-time fault detection of industrial systems

Thanks to the pervasive deployment of sensors in Industry 4.0, data-driven methods are recently playing an important role in the fault diagnosis and prognosis of industrial systems. In this paper, a novel Adaptive Constrained Clustering algorithm is defined to support real-time fault detection of an industrial machine, by clustering the incoming monitoring data into two clusters over time, representing the nominal and non-nominal work conditions, respectively. To this aim, the proposed algorithm relies on a two-stage procedure: micro-clustering and constrained macro-clustering. The former stage is responsible for grouping the batches of work-cycle data into micro-clusters, while the data stream continuously arrives from the data acquisition system. Then, after condensing the micro-clusters into vectors of cluster features, and leveraging on additional knowledge on the nominal and non-nominal working conditions (i.e., constraints on some samples), the second stage aims at offline grouping the micro-clusters features into macro-clusters. Experimental results on a real-world industrial case study show that the proposed real time framework achieves the same results of offline baseline methods (e.g., Constrained K-means) with a higher responsiveness and processing speed; in comparison to stream baseline methods (e.g., Stream K-means), the proposed approach obtains more accurate and easily interpretable results.

A constrained clustering approach to bounded-error identification of switched and piecewise affine systems

This paper proposes a novel clustering-based approach to the bounded-error identification of switched and piecewise affine autoregressive exogenous systems. We address the problem of determining a minimal collection of linear-in-the-parameters models (called modes) fitting with a given accuracy ɛ a set of input–output data while complying with the switched or piecewise affine nature of the system. The problem is tackled by suitably clustering the data according to their preferences with respect to a pool of candidate models identified on subsets of the available data. The preference of a data point for a model is assessed based on the extent to which that model fits that data point and is set to zero if the fit is worse than ɛ. A two-level clustering with outliers isolation is employed, first grouping data based on their preferences subject to suitable time/space adjacency conditions depending on the nature of the switching mechanism, and then collecting together non-adjacent clusters that can be described by the same mode. The performance of the proposed method is demonstrated via comparative numerical examples and on experimental data from an electronic component placement process in a pick-and-place machine.

Using the Deep Constrained Clustering Approach to Create a Business Profile

Identification of customers in the business sector that really needs to be done as an evaluation of a business that is run so that it can continue to grow and be able to follow business developments in the same sector. The deep constraint clustering approach is used to cluster customers towards a business. In this study, a clustering of customers using rail mass transportation will be carried out. The results achieved are the formation of 6 clusters using trains be built. The result of research expected to be a consideration in improving services to the company

Constructing segmented rental housing indices: evidence from Beijing, China

PurposeThis paper aims to construct rental housing indices and identify market segmentation for more effective property-management strategies.Design/methodology/approachThe hedonic model was employed to construct the rental indices. Using the k-means++ and REDCAP (Regionalisation with Dynamically Constrained Agglomerative Clustering and Partitioning) approaches, the authors conducted clustering analysis and identified different market segmentation. The empirical study relied on the database of 80,212 actual rental transactions in Beijing, China, spanning 2016–2018.FindingsRental housing market segmentation may distribute across administrative boundaries. Properly segmented indices could provide a better account for the heterogeneity and spatial continuity of rental housing and as well be crucial for effective property management.Research limitations/implicationsResidential rent might not only vary over space but also interplays with housing price. It would be worth studying how the rental market functions together with the owner-occupied sector in the future.Practical implicationsResidential rental indices are of great importance for policymakers to be able to evaluate housing policies and for property managers to implement competitive strategies in the rental market. Their constructions largely depend on the analysis of market segmentation, a trade-off between housing spatial heterogeneity and continuity.Originality/valueThis paper fills the gap in knowledge concerning segmented rental indices construction, particularly in China. The spatial constrained clustering approach (REDCAP) was also initially introduced to identify regionalised market segmentation due to its superior performance.

Classification of Markov Encrypted Traffic on Gaussian Mixture Model Constrained Clustering

In order to solve the problem that traditional analysis approaches of encrypted traffic in encryption transmission of network application only consider the traffic classification in the complete communication process with ignoring traffic classification in the simplified communication process, and there are a lot of duplication problems in application fingerprints during state transition, a new classification approach of encrypted traffic is proposed. The article applies the Gaussian mixture model (GMM) to analyze the length of the message, and the model is established to solve the problem of application fingerprint duplication. The fingerprints with similar lengths of the same application are divided into as few clusters as possible by constrained clustering approach, which speeds up convergence speed and improves the clustering effect. The experimental results show that compared with the other encryption traffic classification approaches, the proposed approach has 11.7%, 19.8%, 6.86%, and 5.36% improvement in TPR, FPR, Precision, and Recall, respectively, and the classification effect of encrypted traffic is significantly improved.

Multi-modal constraint propagation via compatible conditional distribution reconstruction

In many applications, the pairwise constraint is a kind of weaker supervisory information which can be collected more efficiently than the label information. The constraint propagation is a constrained clustering approach, which has been proved to be a success of exploiting such side-information. Recent years have witnessed many methods of multi-modal constraint propagation. However, the problem of reasonably fusing different modalities under the framework of constraint propagation remains unaddressed. In this paper, we first identify a necessary and sufficient condition for compatible conditional distributions under a specific assumption and address the problem of Compatible Conditional Distributions Reconstruction (CCDR). With the help of CCDR, we propose a multi-modal constraint propagation method dubbed Instance Level Multi-Modal Constraint Propagation (ILMCP). ILMCP fuses the affinity of different modalities at the data instance level instead of the modality level and constructs a unified affinity matrix. Extensive experiments on two publicly available multi-modal datasets show the superior performance of the proposed method.

A constrained agglomerative clustering approach for unipartite and bipartite networks with application to credit networks

Researchers and practitioners have been interested in solving real-world problems through clustering. The clustering of nodes in networks with unipartite or bipartite structure is important to explore real-world complex networks present in nature and society. Bipartite networks form an important class of complex networks because they reveal the heterogeneity of nodes in a network. However, most extant clustering methods focus only on unipartite networks. In this work, a novel constrained agglomerative clustering method applicable to unipartite and bipartite networks has been proposed. Initially, the topology of a network is modeled according to set-theoretic principles. Subsequently, the concepts related to rough set theory and relative linkage are used to cluster the set of nodes. The utility and effectiveness of the proposed approach are demonstrated through offline experiments on unipartite and bipartite networks. A comparison against ten state-of-the-art similarity measures over two different partitional clustering algorithms reveals the effectiveness of the proposed relative linkage measure. Moreover, a comparative analysis with state-of-the-art network clustering methods reveals the viability of the proposed rough set-based constrained agglomerative clustering algorithm. Finally, the proposed method has been applied for the detection of cohesive subgroups of banks in a real bipartite network formed by mapping credit relationships between Indian firms and banks.

Biogeographic classification of streams using fish community– and trait–environment relationships

AbstractAimPresent a hybrid biogeographic and environmentally constrained clustering approach to classify ~853,000 stream reaches in the eastern United States. Examine the frequency of stream typologies in the landscape relative to anthropogenic stressors to identify potential stream conservation needs.LocationEastern United States.MethodsFish communities at 956 least‐disturbed sampling reaches were characterized using taxonomic and functional composition of native species. Environmental variables summarized within stream reaches included stream discharge, channel gradient, hydrologic regime, summer water temperature and boundaries of freshwater ecoregions. Multivariate regression trees were used to relate environmental variables to fish communities while simultaneously developing taxa‐ and trait‐specific biogeographic classifications. We then overlaid stream classifications with indices of anthropogenic disturbances to evaluate rarity and risk of complete loss of natural representation of stream types.ResultsTaxonomically based classes represented a combination of ecoregional boundaries and environmental gradients, whereas temperature gradients were most important in differentiating functional composition. An optimal taxonomy‐based classification contained 13 classes and explained 26.4% of community variation, and the optimal trait‐based classification contained 6 classes, explaining 25.2% of community variation. Overlaying class maps with an anthropogenic disturbance index revealed substantial variation in the severity of degradation among classes relative to their abundance in the landscape, especially rare classes (i.e., large rivers and cold headwater systems).Main conclusionsWe demonstrate the value of integrating local, regional and historical biogeographic factors with taxonomic and functional characteristics of freshwater biota to classify ecosystems based on mechanistic biota–environment relationships. This approach is important to addressing the challenge of developing stream classifications that reconcile the environmental drivers of community trait composition with the constraints of biogeographic regionality at large spatial scales. Furthermore, the spatially explicit and high‐resolution maps disseminated here can uniquely inform management decisions regarding anthropogenic threats to the biodiverse freshwaters of the eastern United States.

Insights on strategic air taxi network infrastructure locations using an iterative constrained clustering approach

Expected to launch globally in the forthcoming years, air taxis are brand-new aviation ridesharing services that will be provided by international logistic pioneers. This study is one of the first to estimate the demand for such a service and provide recommendations on the potential location of facilities to support this network operations. We adopt a two-phase approach: phase-1 estimates the demand for air taxi services by taking a subset of the regular taxi customers who are likely to avail this service, and phase-2 proposes a constrained clustering approach, with multimodal transportation-based warm start technique, to identify potential sites for locating infrastructures based on the estimated demand. We test the feasibility of the proposed approach using millions of real-life New York City taxi records. Results indicate that large facilities with a capacity of nearly 150 landings/hour have to be established in JFK International Airport and South Central Park, while smaller stops are required in World Trade Center, Washington Square and Allerton Ballfields. In addition, we evaluate the impact of the commuter’s “willingness to fly” rate, demand fulfillment rate and time-cost tradeoffs. Our analysis shows that the percentage of time savings and “willingness to fly” rate did not significantly impact location decisions and the number of sites, while it is necessary to conduct an intense market study to determine on-road travel limits. Insights provided in this study can act as a decision support tool for any logistics company that is interested in venturing into the air taxi market.

Regional air pollution mixtures across the continental US

A limited literature body have estimated regional differences in air pollution mixtures. More comprehensive analyses are necessary to accurately depict differences in air pollution characteristics over space and time. Our objective is to further these efforts by investigating spatial differences of air pollution mixtures across the US. We employed spatially constrained clustering approach (based on k-means algorithm) to group air pollution monitoring sites that exhibit distinct pollutant profiles or mixtures in the US over 9 years (2008–2016). We accounted for 20 chemical components of PM2.5. The resulting clusters of pollution mixtures are characterized and validated based on source emissions represented by land-use information. Our analysis resulted in 27 clusters with different number of sites. For example, the cluster 1 has 14 sites and it covers part of the southeast, including the states of North Carolina, South Carolina, Georgia, and Florida. The southwest has a very prominent cluster with 8 sites (cluster 26), covering part of the Louisiana, Mississippi, Texas, and Arkansas. In the west coast, two clusters were highlighted in our analysis, cluster 3 in California and cluster 7 in Washington and part of Oregon. Both clusters with 5 sites. We estimated that Cu, Se, NO3−, Cr, and Ba were the top five species that divided the study area into cluster of sites more effectively. Observing the concentration ratios (concentrations of the species i/concentration of PM2.5) for some of these clusters, our results show that clusters 3 and 7 in the west coast represent sites with high Na ratios. Cluster 13 in the northwest and part of the Midwest represents sites with high SO42− ratio. The cluster 16 with a single site in northeast has the highest SO42− ratio, representing almost the third quartile of the SO42− ratio. This is one of the few studies focused on spatial patterns analysis to estimate regions that exhibit distinct pollutant mixtures on a large scale. We expect that further investigations can use our findings to analyze the relationship between areas that exhibit distinct pollutant mixtures and the impact of regulations, climate change, and health effects in the US.

Image Cosegmentation via Saliency-Guided Constrained Clustering with Cosine Similarity

Cosegmentation jointly segments the common objects from multiple images. In this paper, a novel clustering algorithm, called Saliency-Guided Constrained Clustering approach with Cosine similarity (SGC3), is proposed for the image cosegmentation task, where the common foregrounds are extracted via a one-step clustering process. In our method, the unsupervised saliency prior is utilized as a partition-level side information to guide the clustering process. To guarantee the robustness to noise and outlier in the given prior, the similarities of instance-level and partition-level are jointly computed for cosegmentation. Specifically, we employ cosine distance to calculate the feature similarity between data point and its cluster centroid, and introduce a cosine utility function to measure the similarity between clustering result and the side information. These two parts are both based on the cosine similarity, which is able to capture the intrinsic structure of data, especially for the non-spherical cluster structure. Finally, a K-means-like optimization is designed to solve our objective function in an efficient way. Experimental results on two widely-used datasets demonstrate our approach achieves competitive performance over the state-of-the-art cosegmentation methods.

Ensemble image registration by a spatially constrained clustering approach

In this article, a novel spatially constrained clustering approach is proposed for ensemble image registration. We use a spatially constrained Gaussian mixture model, which is based on a joint Gaussian mixture model and Markov random field, to model the joint intensity scatter plot of the unregistered images. The spatially constrained Gaussian mixture model has the capability of performing the correlation among neighboring observations. A cost function of reducing the dispersion in the joint intensity scatter plot is proposed using the spatially constrained Gaussian mixture model to simultaneously register a group of images. We derive an expectation maximization algorithm for the proposed model. Computer simulations demonstrate the effectiveness of the proposed method.

Constrained clustering for flocking-based tracking in maneuvering target environment

Self-organizing ability is one of the most important requirements of modern sensor networks; particularly for tracking maneuvering targets. Flocking-based approaches are biologically inspired methods that have recently gained significant attention to address the control and coordination problem in self-organizing sensor networks. These approaches are exemplified by the two well-known algorithms, namely, the Flocking and the Semi-Flocking algorithms. Although these two algorithms have demonstrated promising performance in tracking linear target(s), they have deficiencies in tracking maneuvering targets.This paper introduces a constrained clustering approach that uses a novel extension of K-means algorithm to provide better coverage over maneuvering targets. This extension clusters the sensors based on certain background knowledge, then uses the information about the clusters to improve coverage. The performances of flocking-based algorithms, both with and without the proposed approach, are examined in tracking both linear and maneuvering targets. Experimental results demonstrate how constrained clustering yields better tracking of maneuvering targets, and how applying constraints on the clustering process improves the quality of clustering and increases the speed of convergence.

Mining contentious documents

This work proposes an unsupervised method intended to enhance the quality of opinion mining in contentious text. It presents a Joint Topic Viewpoint (JTV) probabilistic model to analyze the underlying divergent arguing expressions that may be present in a collection of contentious documents. It extends the original Latent Dirichlet Allocation, which makes it domain and thesaurus independent, e.g., does not rely on WordNet coverage. The conceived JTV has the potential of automatically carrying the tasks of extracting associated terms denoting an arguing expression, according to the hidden topics it discusses and the embedded viewpoint it voices. Furthermore, JTV's structure enables the unsupervised grouping of obtained arguing expressions according to their viewpoints, using a constrained clustering approach. Experiments are conducted on three types of contentious documents: polls, online debates and editorials. The qualitative and quantitative analyses of the experimental results show the effectiveness of our model to handle six different contentious issues when compared to a state-of-the-art method. Moreover, the ability to automatically generate distinctive and informative patterns of arguing expressions is demonstrated. Furthermore, the coherence of these arguing expressions is proved to be of a high quality when evaluated on the basis of recently introduced automatic coherence measure.

A new knowledge-based constrained clustering approach: Theory and application in direct marketing

Clustering has always been an exploratory but critical step in the knowledge discovery process. Often unsupervised, the clustering task received a huge interest when reinforced by different kinds of inputs provided by the user. This paper presents an approach giving the possibility to incorporate business knowledge in order to guide the clustering algorithm. A formalization of the fact that an intuitive a priori prioritization of the variables might exist, is presented in this paper and applied in a direct marketing context using recent data. By providing the analyst with a new approach offering different clustering perspectives, this paper proposes a straightforward way to apply constrained clustering with soft attribute-level constraints based on feature order preferences.

MRI brain image segmentation and bias field correction based on fast spatially constrained kernel clustering approach

A fast spatially constrained kernel clustering algorithm is proposed for segmenting medical magnetic resonance imaging (MRI) brain images and correcting intensity inhomogeneities known as bias field in MRI data. The algorithm using kernel technique implicitly maps image data to a higher dimensional kernel space in order to improve the separability of data and provide more potential for effectively segmenting MRI data. Based on the technique, a speed-up scheme for kernel clustering and an approach for correcting spurious intensity variation of MRI images have been implemented. The fast kernel clustering and bias field correcting benefit each other in an iterative matter and have dramatically reduced the time complexity of kernel clustering. The experiments on simulated brain phantoms and real clinical MRI data have shown that the proposed algorithm generally outperforms the corresponding traditional algorithms when segmenting MRI data corrupted by high noise and gray bias field.

Structural Segmentation of Musical Audio by Constrained Clustering

We describe a method of segmenting musical audio into structural sections based on a hierarchical labeling of spectral features. Frames of audio are first labeled as belonging to one of a number of discrete states using a hidden Markov model trained on the features. Histograms of neighboring frames are then clustered into segment-types representing distinct distributions of states, using a clustering algorithm in which temporal continuity is expressed as a set of constraints modeled by a hidden Markov random field. We give experimental results which show that in many cases the resulting segmentations correspond well to conventional notions of musical form. We show further how the constrained clustering approach can easily be extended to include prior musical knowledge, input from other machine approaches, or semi-supervision.