Distance-based Clustering Algorithm Research Articles

Parameter-free basis allocation for efficient multiple metric learning

Metric learning involves learning a metric function for distance measurement, which plays an important role in improving the performance of classification or similarity-based algorithms. Multiple metric learning is essential for efficiently reflecting the local properties between instances, as single metric learning has limitations in reflecting the nonlinear structure of complex datasets. Previous research has proposed a method for learning a smooth metric matrix function through data manifold to address the challenge of independently learning multiple metrics. However, this method uses the basic distance-based clustering algorithm to set the anchor points, which are the basis for local metric learning, and the number of basis metrics is dependent on the user. We propose a new method that can assign sophisticated anchor points by iteratively partitioning to identify mixed clusters of multi-class instances and cluster the most similar class instances together. In an experiment, we demonstrate the reliability of the automatically set parameter by comparison with the distribution of error rates according to the number of basis metrics of the existing algorithm. Furthermore, we show the superior performance of the proposed method over a fixed parameter setting of existing algorithms and confirm the relative classification accuracy superiority through performance comparison with baseline algorithms.

Time-Series-Based Feature Selection and Clustering for Equine Activity Recognition Using Accelerometers

With over 16 million horses worldwide and nearly 60,000 sport horses registered to the International Federation for Equestrian Sports database, tracking the activities and performance of these equines is becoming an important aspect in horse management. To perform this activity recognition, Inertial Measurement Units (IMUs) are often used in combination with machine learning algorithms. These often require large labeled datasets to be trained. To this end, a data-efficient algorithm is proposed that requires only 3 minutes of labeled calibration data. This is achieved by combining supervised feature selection, using the tsfresh time-series feature calculation library and the Kendall rank correlation coefficient, with a distance-based clustering algorithm. The generalizability performance of the algorithm is tested by evaluating on a dataset captured with leg-mounted IMUs and on a dataset captured using a neck-mounted IMU. On both datasets, the algorithm achieved accuracies of 95%, comparable to state-of-the-art deep learning approaches, when calibrating and evaluating using the same horse. When the algorithm was calibrated on data from multiple horses and evaluated on horses that were not in the calibration dataset, a 15% drop in classification accuracy is observed. The proposed algorithm is compared with fully supervised algorithms like convolutional neutral network, support vector machine, and random forest in terms of accuracy achieved with respect to the size of the labeled data using calibration. Our approach achieved accuracies that were similar to these classical algorithms whilst only using 10–15% the amount of labeled data.

Goal model structuring based on semantic correlation of user reviews

User review is a crucial component of open mobile app market, such as the Google Play Store. These markets allow users to submit reviews for downloaded apps. By analyzing these reviews, app developers can find new or missing features. However, owing to the huge number of reviews, this manual process is time-consuming and unscalable. An automatic method for user review clustering and goal-model identification has been proposed. However, this method limits the numbers of sub-goals in the goal model. To address this, we propose a comprehensive user review clustering method. This method comprises two components. One is a latent Dirichlet allocation (LDA) model, which clusters user reviews into several topics. The other is a distance-based clustering algorithm, which is an improved version of the existing clustering method.

DIRICHLET-TREE MULTINOMIAL MIXTURES FOR CLUSTERING MICROBIOME COMPOSITIONS.

Studying the human microbiome has gained substantial interest in recent years, and a common task in the analysis of these data is to cluster microbiome compositions into subtypes. This subdivision of samples into subgroups serves as an intermediary step in achieving personalized diagnosis and treatment. In applying existing clustering methods to modern microbiome studies including the American Gut Project (AGP) data, we found that this seemingly standard task, however, is very challenging in the microbiome composition context due to several key features of such data. Standard distance-based clustering algorithms generally do not produce reliable results as they do not take into account the heterogeneity of the cross-sample variability among the bacterial taxa, while existing model-based approaches do not allow sufficient flexibility for the identification of complex within-cluster variation from cross-cluster variation. Direct applications of such methods generally lead to overly dispersed clusters in the AGP data and such phenomenon is common for other microbiome data. To overcome these challenges, we introduce Dirichlet-tree multinomial mixtures (DTMM) as a Bayesian generative model for clustering amplicon sequencing data in microbiome studies. DTMM models the microbiome population with a mixture of Dirichlet-tree kernels that utilizes the phylogenetic tree to offer a more flexible covariance structure in characterizing within-cluster variation, and it provides a means for identifying a subset of signature taxa that distinguish the clusters. We perform extensive simulation studies to evaluate the performance of DTMM and compare it to state-of-the-art model-based and distance-based clustering methods in the microbiome context, and carry out a validation study on a publicly available longitudinal data set to confirm the biological relevance of the clusters. Finally, we report a case study on the fecal data from the AGP to identify compositional clusters among individuals with inflammatory bowel disease and diabetes. Among our most interesting findings is that enterotypes (i.e., gut microbiome clusters) are not always defined by the most dominant species as previous analyses had assumed, but can involve a number of less abundant OTUs, which cannot be identified with existing distance-based and method-based approaches.

A spatial distance-based spatial clustering algorithm for sparse image data

By allocating each object to one of the predefined categories, image classification deeply understands the attributes and features of the data on each object in the scene, and further mines the potential features and internal connections of the data, supporting the subsequent application decision-making with necessary structured data. One of the key challenges to image classification is how to accurately classify sparse data, when there is an imbalance between different categories of data, i.e., how to identify small objects in images. Recognizing a person in satellite images is such a challenging task. These objects are sparse either globally or in each recognizable local segment. Therefore, they are often overlooked by the classifier, or removed as noises. During deep learning, feature sparsity means the samples contain too much useless information, which suppresses the generalization and accuracy of the model. To solve the problem, this paper presents a spatial distance-based spatial clustering algorithm for sparse image data (SDBSCA-SID). Firstly, the imaging range of the image sensor constitutes a two-dimensional (2D) constraint space. Under the constraint, spatial clustering was carried out based on the features of each sample to aggregate dense data into primary categories, and aggregate sparse data and noises into secondary categories. Referring to the 2D constrained space, multiple spatial classification surfaces were constructed to aggregate the sparse data to the two sides of these surfaces as much as possible. If the error is minimized, then the sparse data belong to these classification surfaces. To shorten the convergence time of the clustering algorithm on imbalanced data, the original sample set was cut into slices, and assigned to several calculation units for separate clustering. Next, the same-class clusters were merged through reduction. Finally, the obtained class labels were compared with the preset class labels, wrapping up the semantic segmentation of images. The stability and accuracy of our algorithm were demonstrated through tests on image samples.

Importance of Numerical Implementation and Clustering Analysis in Force-Directed Algorithms for Accurate Community Detection

Real-world networks show community structures – groups of nodes that are densely intra-connected and sparsely inter-connected to other groups. Nevertheless, Community Detection (CD) is non-trivial, since identifying these groups of nodes according to their local connectivity can hold many plausible solutions, leading to the creation of different methods. In particular, CD has recently been achieved by Force-Directed Algorithms (FDAs), which originally were designed as a way to visualize networks. FDAs map the network nodes as particles in a D-dimensional space that are affected by forces acting in accordance to the connectivity. However, the literature on FDA-based methods for CD has grown in parallel from the classical methods, leaving several open questions, such as how accurately FDAs can recover communities compared to classical methods. In this work, we start to fill these gaps by evaluating different numerical implementations of 5 FDA methods and different clustering analyses on state-of-the-art network benchmarks – including networks with or without weights and networks with a hierarchical organisation. We also compare these results with 8, well-known, classical CD methods. Our findings show that FDA methods can achieve higher accuracy than classical methods, albeit their effectiveness depends on the chosen setting – with optimisation techniques leading over numerical integration and distance-based clustering algorithms leading over density-based ones. Overall, our work provides detailed information for any researcher aiming to apply FDAs for community detection.

An analysis of classical multidimensional scaling with applications to clustering.

Classical multidimensional scaling is a widely used dimension reduction technique. Yet few theoretical results characterizing its statistical performance exist. This paper provides a theoretical framework for analyzing the quality of embedded samples produced by classical multidimensional scaling. This lays a foundation for various downstream statistical analyses, and we focus on clustering noisy data. Our results provide scaling conditions on the signal-to-noise ratio under which classical multidimensional scaling followed by a distance-based clustering algorithm can recover the cluster labels of all samples. Simulation studies confirm these scaling conditions are sharp. Applications to the cancer gene-expression data, the single-cell RNA sequencing data and the natural language data lend strong support to the methodology and theory.

An Optimized K-means with Density and Distance-Based Clustering Algorithm for Multidimensional Spatial Databases

From massive and complex spatial database, the useful information and knowledge are extracted using spatial data mining. To analyze the complexity, efficient clustering algorithm for spatial database has been used in this area of research. The geographic areas containing spatial points are discovered using clustering methods in many applications. With spatial attributes, the spatial clustering problem have been designed using many approaches, but non-overlapping constraints are not considered. Most existing data mining algorithms suffer in high dimensions. With non-overlapping named as Non Overlapping Constraint based Optimized K-Means with Density and Distance-based Clustering (NOC-OKMDDC),a multidimensional optimization clustering is designed to solve this problem by the proposed system and the clusters with diverse shapes and densities in spatial databases are fast found. Proposed method consists of three main phases. Using weighted convolutional Neural Networks(Weighted CNN), attributes are reduced from the multidimensional dataset in this first phase. A partition-based algorithm (K-means) used by Optimized K-Means with Density and Distance-based Clustering (OKMDD) and several relatively small spherical or ball-shaped sub clusters are made by Clustering the dataset in this second phase. The optimal sub cluster count is performed with the help of Adaptive Adjustment Factor based Glowworm Swarm Optimization algorithm (AAFGSO). Then the proposed system designed an Enhanced Penalized Spatial Distance (EPSD) Measure to satisfy the non-overlapping condition. According to the spatial attribute values, the spatial distance between two points are well adjusted to achieving the EPSD. In third phase, to merge sub clusters the proposed system utilizes the Density based clustering with relative distance scheme. In terms of adjusted rand index, rand index, mirkins index and huberts index, better performance is achieved by proposed system when compared to the existing system which is shown by experimental result.

An Effective Analysis of Data Clustering using Distance-based K- Means Algorithm

Real-world data sets are regularly provides different and complementary features of information in an unsupervised way. Different types of algorithms have been proposed recently in the genre of cluster analysis. It is arduous to the user to determine well in advance which algorithm would be the most suitable for a given dataset. Techniques with respect to graphs are provides excellent results for this task. However, the existing techniques are easily vulnerable to outliers and noises with limited idea about edges comprised in the tree to divide a dataset. Thus, in some fields, the necessacity for better clustering algorithms it uses robust and dynamic methods to improve and simplify the entire process of data clustering has become an important research field. In this paper, a novel distance-based clustering algorithm called the entropic distance based K-means clustering algorithm (EDBK) is proposed to eradicate the outliers in effective way. This algorithm depends on the entropic distance between attributes of data points and some basic mathematical statistics operations. In this work, experiments are carry out using UCI datasets showed that EDBK method which outperforms the existing methods such as Artificial Bee Colony (ABC), k-means.

Distance-based K-Means Clustering Algorithm for Anomaly Detection in Categorical Datasets

Distance-based K-Means Clustering Algorithm for Anomaly Detection in Categorical Datasets

Scenario reduction methodologies under uncertainties for reservoir development purposes: distance-based clustering and metaheuristic algorithm

The simulation process under uncertainty needs numerous reservoir models that can be very time-consuming. Hence, selecting representative models (RMs) that show the uncertainty space of the full ensemble is required. In this work, we compare two scenario reduction techniques: (1) Distance-based Clustering with Simple Matching Coefficient (DCSMC) applied before the simulation process using reservoir static data, and (2) metaheuristic algorithm (RMFinder technique) applied after the simulation process using reservoir dynamic data. We use these two methods as samples to investigate the effect of static and dynamic data usage on the accuracy and rate of the scenario reduction process focusing field development purposes. In this work, a synthetic benchmark case named UNISIM-II-D considering the flow unit modelling is used. The results showed both scenario reduction methods are reliable in selecting the RMs from a specific production strategy. However, the obtained RMs from a defined strategy using the DCSMC method can be applied to other strategies preserving the representativeness of the models, while the role of the strategy types to select the RMs using the metaheuristic method is substantial so that each strategy has its own set of RMs. Due to the field development workflow in which the metaheuristic algorithm is used, the number of required flow simulation models and the computational time are greater than the workflow in which the DCSMC method is applied. Hence, it can be concluded that static reservoir data usage on the scenario reduction process can be more reliable during the field development phase.

Coarse-grained hybrid Monte Carlo/molecular dynamics simulations of the nanophase separated morphology of polysulfone grafted with phosphonated sidechain for proton exchange membrane fuel cells

Nanophase separated morphology is a widely accepted design for the balance of proton conductivity and integrity of proton exchange membrane for fuel cells (PEMFC). Since the coarse-grained (CG) modeling goes beyond the time- and space-scales accessible to the atomistic modeling and the Monte Carlo simulation (MC) overcomes the steric hindrance that molecular dynamics simulation (MD) faces during the rearrangements of polymeric chains, the CG hybrid MC/MD searches effectively the configurational space and can be applied to the investigation of nanophase separated morphology of proton exchange membrane. In this work, the morphological characteristics of proton exchange membrane of poly(vinyl phosphonic acid) grafted polysulfone (PSU-g-PVPA) are studied using CG hybrid MC/MD and analyzed using distance-based clustering algorithm. It is revealed that the PVPA hydrophilic sidechains and the PSU hydrophobic mainchains segregate, respectively, into hydrophilic/hydrophobic clusters resulting nanophase separated morphology upon hydration. It is concluded that a length of five repeat units for the PVPA sidechains is necessary for the hydrophilic/hydrophobic nanophase separation, and the hydrophilic clusters connect into continuous channels for the long-distance transport of protons. Finally, the effective local hydration degrees (λi/λ0) in the four largest hydrophilic clusters are evaluated to increase from 42.2% to 48.0% and 48.7% as the PVPA sidechain length increases from 1 to 5 and 10.

IMPLEMENTATION OF CLUSTERING USING K-MEANS METHOD TO DETERMINE NUTRITIONAL STATUS

Cluster analysis aims to classify data objects into two categories: objects that are similar in characteristics in one cluster and objects that are different in characteristics with the other objects of another cluster. K-Means is a method included in the distance-based clustering algorithm that starts by determining the number of desired clusters. Malnutrition is one of the biggest concerns in Indonesia. According to Riskesdas 2018 data, as many as 17.7% infants under 60-month-old are still having problems with nutrition intake while 3.9% are having malnutrition. This might result in higher death rate. This research was conducted to classify the nutritional status of infants under 60-month-old conducted by the C-Means Clustering method. This research is non-reactive, using secondary data in Ponkesdes Mayangrejo, Bojonegoro without direct interaction with the subject. This study concluded that the grouping of nutritional status is possible by using K-Means with 4 clusters formed which are 23 malnourished toddlers, 17 undernourished toddlers, 7 nourished toddlers, and 10 over-nourished toddlers.

A DTW distance-based seismic waveform clustering method for layers of varying thickness

Seismic waveform clustering is a useful technique for lithologic identification and reservoir characterization. The current seismic waveform clustering algorithms are predominantly based on a fixed time window, which is applicable for layers of stable thickness. When a layer exhibits variable thickness in the seismic response, a fixed time window cannot provide comprehensive geologic information for the target interval. Therefore, we propose a novel approach for a waveform clustering workflow based on a variable time window to enable broader applications. The dynamic time warping (DTW) distance is first introduced to effectively measure the similarities between seismic waveforms with various lengths. We develop a DTW distance-based clustering algorithm to extract centroids, and we then determine the class of all seismic traces according to the DTW distances from centroids. To greatly reduce the computational complexity in seismic data application, we propose a superpixel-based seismic data thinning approach. We further propose an integrated workflow that can be applied to practical seismic data by incorporating the DTW distance-based clustering and seismic data thinning algorithms. We evaluated the performance by applying the proposed workflow to synthetic seismograms and seismic survey data. Compared with the the traditional waveform clustering method, the synthetic seismogram results demonstrate the enhanced capability of the proposed workflow to detect boundaries of different lithologies or lithologic associations with variable thickness. Results from a practical application show that the planar map of seismic waveform clustering obtained by the proposed workflow correlates well with the geological characteristics of wells in terms of reservoir thickness.

Group Labeling Methodology Using Distance-based Data Grouping Algorithms

Clustering algorithms are often used to form groups based on the similarity of their members. In this context, understanding a group is just as important as its composition. Identifying, or labeling groups can assist with their interpretation and, consequently, guide decision-making efforts by taking into account the features from each group. Interpreting groups can be beneficial when it is necessary to know what makes an element a part of a given group, what are the main features of a group, and what are the differences and similarities among them. This work describes a method for finding relevant features and generate labels for the elements of each group, uniquely identifying them. This way, our approach solves the problem of finding relevant definitions that can identify groups. The proposed method transforms the standard output of an unsupervised distance-based clustering algorithm into a Pertinence Degree (GP), where each element of the database receives a GP concerning each formed group. The elements with their GPs are used to formulate ranges of values for their attributes. Such ranges can identify the groups uniquely. The labels produced by this approach averaged 94.83% of correct answers for the analyzed databases, allowing a natural interpretation of the generated definitions.

Uto-Aztecan Lexicostatistics 2.0

Uto-Aztecan subgrouping has long been the subject of debate. We aim to establish a more up-to-date foundation for Uto-Aztecan lexicostatistics by reexamining Wick Miller’s influential lexicostatistic classification. Miller’s cognate density measure yields a symmetrical table based on the number of cognates each language pairing shares on a modified Swadesh-100 wordlist. However, no language has cognate sets for all word meanings (glosses) on that list. We offer an improved metric, relative cognate density, for analyzing an updated database of Kenneth Hill’s Uto-Aztecan cognate sets. We generate an asymmetrical table dividing the number of pairwise shared cognates (“match counts”) by the number of cognate sets each comparison language actually has available. Employing more standard distance-based clustering algorithms (UPGMA, Neighbor Joining, and NeighborNet), our results align with Miller’s in some respects (e.g., in identifying a Southern-Uto-Aztecan branch but not a Northern-Uto-Aztecan one) but differ in others (e.g., in not identifying “Sonoran”).

Energy Reckoning Distance Based Clustering for Spectrum Aware Cognitive Radio Wireless Sensor Networks

Implementing cognitive radio sensor nodes in wireless sensor networks introduced a smart combination called cognitive radio sensor network (CRSN) which creates new challenges in the design of network topology. Conserving the nodes energy helps to extend the lifetime of the network. This stands as an important criterion while designing any algorithm. In order to achieve the same, two important criteria are to be considered – the communicating distance between the nodes or node to base station and proper spectrum sharing technique. In the proposed work, Energy Reckoning Distance-Based Clustering (ERDBC) algorithm, both the criterion is taken into consideration and designed in order to increase the lifetime of a cognitive radio sensor network. In the ERDBC algorithm, the whole network area is divided into three regions according to the distance and the cluster heads are elected based on energy, distance and common channel creates a greater impact on retaining the nodes energy. Also, implementing multi-hop routing using proper spectrum sharing technique helps to avoid data collision and retransmission thereby; the energy consumption of the nodes are reduced to a greater extent. The performance of the proposed ERDBC algorithm is measured on the basis of residual energy, throughput, channel usage, first node death, last node death, and network lifetime, and compared with the already existing LEACH, CogLEACH, LEAUCH and CEED algorithms. Thus the network lifetime of the proposed ERDBC algorithm is 78.18% more than LEACH, 73.6% more than CogLEACH, 29.88% more than CEED and 17.98% more than LEAUCH algorithms.

Modified FDP cluster algorithm and its application in protein conformation clustering analysis

We present a modified find density peaks (MFDP) clustering algorithm. In the MFDP, a critical parameter, dc, is auto-defined by minimizing the entropy of all points. By considering both the point density, ρ, and large distance from points with higher densities, δ, the high-dimensional points are transformed into a 2D space. The halo points of the original FDP cluster algorithm are redefined, and a definition of boundary points is introduced to illustrate the intersection region between clusters. To demonstrate the clustering ability, the distance-based K-means clustering and density-based algorithms DBSCAN, original FDP are employed respectively. Four criteria are introduced to evaluate the clustering algorithms quantitatively. For most of the cases, the MFDP provides a superior clustering result than both of the typical clustering algorithms, and FDP in 20 commonly used benchmark datasets, particularly in clearly depicting the intersection region between clusters. Finally, we evaluate the performance of the MFDP in the cluster analysis of conformations in molecular dynamics (MD). In the MD clustering process, eight typical cluster center conformations are selected in six collective variable spaces. Moreover, it is in strong agreement with the experiment results. The clustering results demonstrate the potential for generalized applications of the modified algorithm to similar problems.

DyClee: Dynamic clustering for tracking evolving environments

Evolving environments challenge researchers with non stationary data flows where the concepts – or states – being tracked can change over time. This requires tracking algorithms suited to represent concept evolution and in some cases, e.g. real industrial environments, also suited to represent time dependent features. This paper proposes a unified approach to track evolving environments that uses a two-stages distance-based and density-based clustering algorithm. In this approach data samples are fed as input to the distance based clustering stage in an incremental, online fashion, and they are then clustered to form μ-clusters. The density-based algorithm analyses the micro-clusters to provide the final clusters: thanks to a forgetting process, clusters may emerge, drift, merge, split or disappear, hence following the evolution of the environment. This algorithm has proved to be able to detect high overlapping clusters even in multi-density distributions, making no assumption about cluster convexity. It shows fast response to data streams and good outlier rejection properties.

A new data clustering algorithm based on critical distance methodology

A variety of algorithms have recently emerged in the field of cluster analysis. Consequently, based on the distribution nature of the data, an appropriate algorithm can be chosen for the purpose of clustering. It is difficult for a user to decide a priori which algorithm would be the most appropriate for a given dataset. Algorithms based on graphs provide good results for this task. However, these algorithms are vulnerable to outliers with limited information about edges contained in the tree to split a dataset. Thus, in several fields, the need for better clustering algorithms increases and for this reason utilizing robust and dynamic algorithms to improve and simplify the whole process of data clustering has become an urgent need. In this paper, we propose a novel distance-based clustering algorithm called the critical distance clustering algorithm. This algorithm depends on the Euclidean distance between data points and some basic mathematical statistics operations. The algorithm is simple, robust, and flexible; it works with quantitative data that are real-valued, not qualitative, and categorical with different dimensions. In this work, 26 experiments are conducted using different types of real and synthetic datasets taken from different fields. The results prove that the new algorithm outperforms some popular clustering algorithms such as MST-based clustering, K-means, and Dbscan. Moreover, the algorithm can precisely produce more reasonable clusters even when the dataset contains outliers and without specifying any parameters in advance. It also provides a number of indicators to evaluate the established clusters and prove the validity of the clustering.