Probabilistic Clustering Algorithm Research Articles

Two Fuzzy Clustering Algorithms Based on ARMA Model

This study proposes two fuzzy clustering algorithms based on autoregressive moving average (ARMA) model for series data. The first, referred to as Tsallis entropy-regularized fuzzy c-ARMA model (TFCARMA), is created from k-ARMA, a conventional hard clustering algorithm for series data. TFCARMA is motivated by the relationship between the two clustering algorithms for vectorial data: k-means and Tsallis entropy-regularized fuzzy c-means. The second, referred to as q-divergence-based fuzzy c-ARMA model (QFCARMA), is created from ARMA mixtures, a conventional probabilistic clustering algorithm for series data. QFCARMA is motivated by the relationship between the two clustering algorithms for vectorial data: Gaussian mixture model and q-divergence-based fuzzy c-means. Based on numerical experiments using an artificial dataset, we observed the effects of fuzzification parameters in the proposed algorithms and relationship between the proposed and conventional algorithms. Moreover, numerical experiments using seven real datasets compared the clustering accuracy among the proposed and conventional algorithms.

A Novel Bayesian probabilistic distance clustering algorithm

A Novel Bayesian probabilistic distance clustering algorithm

Remote Sensing Image Segmentation Based on Probabilistic Fuzzy Local Information Clustering

Abstract. In this paper, a remote sensing image segmentation based on probabilistic fuzzy local information clustering algorithm is proposed. First, assuming that the spectral measure within the same ground object follows the same probability distribution. The dissimilarity between pixel and object is modeled by the negative logarithm of the Gaussian probability density function. It can improve the noise sensitive problem caused by the Euclidean distance which can only describe the data with isotropic distribution. Then, in order to consider the effect of local spatial constraint, on the one hand, the probability measure is used to modify the local fuzzy factor to establish the dissimilarity measure with spatial constraints. On the other hand, the hidden Markov random field is used to model the prior probability model of pixel membership. Next, the entropy regularization term of the objective function is built by combining the Kullback-Leibler(KL) maximum entropy model to further improve the robustness and noise resistance. The qualitative and quantitative analysis of simulated image and different types of real remote sensing images show that the proposed algorithm can effectively overcome the above problems and further improve the accuracy of image segmentation to over 95%.

A GMDA clustering algorithm based on evidential reasoning architecture

The traditional clustering algorithm is difficult to deal with the identification and division of uncertain objects distributed in the overlapping region, and aimed at solving this problem, the Evidential Clustering based on General Mixture Decomposition Algorithm (GMDA-EC) is proposed. First, the belief classification of target cluster is carried out, and the sample category of target distribution overlapping region is extended. Then, on the basis of General Mixture Decomposition Algorithm (GMDA) clustering, the fusion model of evidence credibility and evidence relative entropy is constructed to generate the basic probability assignment of the target and achieve the belief division of the target. Finally, the performance of the algorithm is verified by the synthetic dataset and the measured dataset. The experimental results show that the algorithm can reflect the uncertainty of target clustering results more comprehensively than the traditional probabilistic partition clustering algorithm.

MRI Clustering Reveals Three ALS Subtypes With Unique Neurodegeneration Patterns.

The purpose of this study was to identify subtypes of amyotrophic lateral sclerosis (ALS) by comparing patterns of neurodegeneration using brain magnetic resonance imaging (MRI) and explore their phenotypes. We performed T1-weighted and diffusion tensor imaging in 488 clinically well-characterized patients with ALS and 338 control subjects. Measurements of whole-brain cortical thickness and white matter connectome fractional anisotropy were adjusted for disease-unrelated variation. A probabilistic network-based clustering algorithm was used to divide patients into subgroups of similar neurodegeneration patterns. Clinical characteristics and cognitive profiles were assessed for each subgroup. In total, 512 follow-up scans were used to validate clustering results longitudinally. The clustering algorithm divided patients with ALS into 3 subgroups of 187, 163, and 138 patients. All subgroups displayed involvement of the precentral gyrus and are characterized, respectively, by (1) pure motor involvement (pure motor cluster [PM]), (2) orbitofrontal and temporal involvement (frontotemporal cluster [FT]), and (3) involvement of the posterior cingulate cortex, parietal white matter, temporal operculum, and cerebellum (cingulate-parietal-temporal cluster [CPT]). These subgroups had significantly distinct clinical profiles regarding male-to-female ratio, age at symptom onset, and frequency of bulbar symptom onset. FT and CPT revealed higher rates of cognitive impairment on the Edinburgh cognitive and behavioral ALS screen (ECAS). Longitudinally, clustering remained stable: at 90.4% of their follow-up visits, patients clustered in the same subgroup as their baseline visit. ALS can manifest itself in 3 main patterns of cerebral neurodegeneration, each associated with distinct clinical characteristics and cognitive profiles. Besides the pure motor and frontotemporal dementia (FTD)-like variants of ALS, a new neuroimaging phenotype has emerged, characterized by posterior cingulate, parietal, temporal, and cerebellar involvement. ANN NEUROL 2022;92:1030-1045.

Probabilistic Optimal Power Flow-Based Spectral Clustering Method Considering Variable Renewable Energy Sources

Power system clustering is an effective method for realizing voltage control and preventing failure propagation. Various approaches are used for power system clustering. Graph-theory-based spectral clustering methods are widely used because they follow a simple approach with a short calculation time. However, spectral clustering methods can only be applied in system environments for which the power generation amount and load are known. Moreover, it is often impossible to sufficiently reflect the influence of volatile power sources (e.g., renewable energy sources) in the clustering. To this end, this study proposes a probabilistic spectral clustering algorithm applicable to a power system, including a photovoltaic (PV) model (for volatile energy sources) and a classification method (for neutral buses). The algorithm applies a clustering method that reflects the random outputs of PV sources, and the neutral buses can be reclassified via clustering to obtain optimal clustering results. The algorithm is verified through an IEEE 118-bus test system, including PV sources.

Correlation of thermal performance of ASTER with the hydrothermal alteration zones: the case of Nisyros volcano, Greece

We aim to investigate the relation between advanced spaceborne thermal emission and reflection radiometer (ASTER) and thermal infrared (TIR) data with the hydrothermal alteration zones within the hydrothermal field of the caldera of Nisyros volcano in Greece. This is achieved by utilizing clustering on a dataset of five ASTER L1T TIR images spanning the period 2003 to 2007. Each one of these images is converted to temperature images, coregistered, and vegetation masked, creating a five-band ASTER temperature image. In the sequel, probabilistic clustering hypothesis is applied to the five-band vegetation masked ASTER temperature image, subset to the area of the hydrothermal field. The probabilistic clustering algorithm identifies three “thermal” clusters that exhibit a satisfactory degree of agreement with the active hydrothermal alteration field of Nisyros. Each cluster corresponds to a different degree of hydrothermal alteration zone. We show the existence of a middle temperature zone (cluster), which exhibits significant overlap with the middle hydrothermal alteration zone, along with a high-temperature zone and a low-temperature zone. This temperature transition from high-temperature cluster to the middle and gradually to low-temperature cluster for the time-spanning of the data is significant as the hydrothermal field of Nisyros was very active during this period. This finding coincides temporally and spatially with the continuous N–S trending (Lakki rupture).

Evaluation of wind farm aggregation using probabilistic clustering algorithms for power system stability assessment

Wind farm integration in large-scale power systems for performing stability assessment requires significant modelling efforts and high computational time. In these cases, wind farm clustering is used to simplify the simulation efforts, but still, it requires a large number and composition of clusters to represent the abrupt change in wind speed and direction. A probabilistic clustering approach could be useful in such a case, which can identify the most probable cluster(s) in a wind regime within a timeframe, such as one year. This paper has presented a probabilistic clustering framework to represent the most recurring aggregated wind farm model throughout the whole year by implementing four clustering algorithms, namely (i) K-means, (ii) hierarchical, (c) fuzzy c-means, and (d) DBSCAN (density-based spatial clustering of applications with noise). The performance of the aggregated wind farm model has been compared with the detailed wind farm model in assessing the small-disturbance, frequency, and voltage stability of a power system. The simulation results show that the aggregated equivalent models (as identified by the probabilistic clustering approach) present the same level of accuracy while performing the simulation 10-times faster. This simulation efficiency could be very useful for performing dynamic studies for large-scale power systems.

Managing data security in fog computing in IoT devices using noise framework encryption with power probabilistic clustering algorithm

Managing data security in fog computing in IoT devices using noise framework encryption with power probabilistic clustering algorithm

PIFHC: The Probabilistic Intuitionistic Fuzzy Hierarchical Clustering Algorithm

Hierarchical clustering techniques help in building a tree-like structure called dendrogram from the data points which can be used to find the closest related data objects. This paper presents a novel hierarchical clustering technique which considers intuitionistic fuzzy sets to deal with the uncertainty present in the data. Instead of using traditional hamming distance or Euclidean distance measure to find the distance between the data points, it employs the probabilistic Euclidean distance measure to propose a novel clustering approach which we term as ‘Probabilistic Intuitionistic Fuzzy Hierarchical Clustering (PIFHC) Algorithm’. The proposed PIFHC algorithm considers probabilistic weights from the data to measure the distances between the data points. Clustering results over UCI datasets show that our proposed PIFHC algorithm gives better cluster accuracies than its existing counterparts. PIFHC efficiently provides improvements of 1%–3.5% in the clustering accuracy compared to other fuzzy hierarchical clustering algorithms for most of the datasets. We further provide experimental results with the real-world car dataset and the Listeria monocytogenes dataset for mouse susceptibility to demonstrate the practical efficacy of the proposed algorithm. For Listeria datasets as well, proposed PIFHC records 1.7% improvement against the state-of-the-art methods The dendrograms formed by the proposed PIFHC algorithm exhibits high cophenetic correlation coefficient with an improvement of 0.75% over others. We provide various AGNES methods to update the distance between merged clusters in the proposed PIFHC algorithm.

Email thread sentiment sequence identification using PLSA clustering algorithm

Email messaging is the most common way of providing effective communication between internauts. Consequently, the total sent and received emails count will be increased. But, the internaut can't remember all such emails. Even though email thread identification approaches give satisfactory benefits to the internauts, but they may fail to alert them for a cause to identify the sentiments behind an email thread. To address, this issue Probabilistic Latent Semantic Analysis clustering algorithm has been used in this paper to identify the email sentiment thread sequence. The sentiment and the thread sequence within the emails have been discovered as clustering sentiment polarity and temporal categories with the help of PLSA clusters. At the initial stage, we used three feature extraction methods, latent semantic analysis (LDA), bag of words (BoW), TF-IDF and SentiWordNet (SWN) lexicon for generating sentiment features of email. Next, Probabilistic Latent Semantic Analysis algorithm is used to form email clusters based on sentiment features. Thus, it helps to identify thread sentiment and sequence of sentiment threads. Email threads give a mechanism by which any user will be able to find out the sequence in the thread on the basis of sentiment analysis of email related to a specific set of communication during a specific time period. Various parameters evaluation measures have been considered in this work to evaluate the proposed model such as accuracy, precision, recall and F-measure, and the proposed algorithm is compared with other standard algorithms. Furthermore, a statistical test has also been performed.

Climatology of Winter Extratropical Cyclones over the Coastal Waters of China

The East China coast is one major cyclogenesis region of extratropical cyclones (ETCs) in boreal winter. In this study, the climatological characteristics of winter ETCs passing over the coastal water of China are analyzed by using clustering and composite analysis. Automated cyclone detection and tracking algorithm are used to identify the ETCs, which are further classified into subgroups according to their trajectory features by using the probabilistic clustering algorithm. Six distinct types of straight moving or recurving trajectories with different climatological characteristics are identified in the clustering analysis. Accordingly, the composite analyses also present six distinct synoptic patterns corresponding to cyclogenesis as well as the impact of ETCs on terrestrial precipitation. The spatio-temporal characteristics of winter ETCs’ origination, movement, and impact are revealed by the clustering and composite analyses.

Inferring Regulatory Programs Governing Region Specificity of Neuroepithelial Stem Cells during Early Hindbrain and Spinal Cord Development.

Neuroepithelial stem cells (NSC) from different anatomical regions of the embryonic neural tube's rostrocaudal axis can differentiate into diverse central nervous system tissues, but the transcriptional regulatory networks governing these processes are incompletely understood. Here, we measure region-specific NSC gene expression along the rostrocaudal axis in a human pluripotent stem cell model of early central nervous system development over a 72-h time course, spanning the hindbrain to cervical spinal cord. We introduce Escarole, a probabilistic clustering algorithm for non-stationary time series, and combine it with prior-based regulatory network inference to identify genes that are regulated dynamically and predict their upstream regulators. We identify known regulators of patterning and neural development, including the HOX genes, and predict a direct regulatory connection between the transcription factor POU3F2 and target gene STMN2. We demonstrate that POU3F2 is required for expression of STMN2, suggesting that this regulatory connection is important for region specificity of NSCs.

Multiple Kernel Probabilistic Clustering with Crow Lion Neural Network for Missing Data Imputation and Classification

In pattern classification, one of the major problems is the missing data or data incompleteness, which is caused by different reasons. The amount of missing data varies depending on the applications. Missing data imputation is a technique used to handle the missing data problem by utilizing various techniques. The missing data imputation techniques replace the missing values by the estimated values. Numerous techniques are proposed for handling the missing data problem. Accordingly, this paper presents the missing data imputation and classification techniques for handling the missing values presented in the data set. Missing data imputation and classification is proposed by integrating the Multiple Kernel Probabilistic Clustering Algorithm (MKPCA) with Feed Crow Lion neural network (CLNN).

Energy Efficient Probabilistic Clustering Technique for Data Aggregation in Wireless Sensor Network

Economic utilization of energy in wireless sensor network, composed of tiny battery powered sensor nodes constrained in energy and computation power is a critical issue. Clustering techniques are most often used to reduce the consumption of energy by the sensor nodes due to data transmission. A widely used class of clustering techniques is probabilistic clustering in which a predetermined optimal probability is used to facilitate the cluster head selection process. This paper aims to devise a technique that improves the energy efficiency of probabilistic clustering algorithms by optimizing the number of clusters and the distribution of cluster heads in the network. We also present two generic approaches to integrate proposed technique into the existing probabilistic clustering algorithms. The simulation results show a considerable improvement in energy efficiency of probabilistic clustering protocols and consequently a prolonged network life time.

The Generalized C Index for Internal Fuzzy Cluster Validity

The C index is an internal cluster validity index that was introduced in 1970 as a way to define and identify a “best” crisp partition on n objects represented by either unlabeled feature vectors or dissimilarity matrix data. This index is often one of the better performers among the plethora of internal indices available for this task. This paper develops a soft generalization of the C index that can be used to evaluate sets of candidate partitions found by either fuzzy or probabilistic clustering algorithms. We define four generalizations based on relational transformations of the soft partition and, then, compare their performance to eight other popular internal fuzzy cluster indices using two methods of comparison (internal “best- c ” and internal/external (I/E) “best match”), six synthetic datasets, and six real-world labeled datasets. Our main conclusion is that the sum-min generalization is the second best performer in the best-c tests and the best performer in the I/E tests on small data.

Mining Spatial Association Rules to Automatic Grouping of Spatial Data Objects Using Multiple Kernel-Based Probabilistic Clustering

AbstractWith the extensive application of spatial databases to various fields ranging from remote sensing to geographical information systems, computer cartography, environmental assessment, and planning, discovery of interesting and hidden knowledge in the spatial databases is a considerable chore for classifying and using the spatial data and knowledge bases. The literature presents different spatial data mining methods to mine knowledge from spatial databases. In this paper, spatial association rules are mined to automatic grouping of spatial data objects using a candidate generation process with three constraint measures, such as support, confidence, and lift. Then, the proposed multiple kernel-based probabilistic clustering is applied to the associate vector to further group the spatial data objects. Here, membership probability based on probabilistic distance is used with multiple kernels, where exponential and tangential kernel functions are utilized. The performance of the proposed method is analyzed with three data sets of three different geometry types using the number of rules and clustering accuracy. From the experimentation, the results proved that the proposed multi-kernel probabilistic clustering algorithm achieved better accuracy as compared with the existing probabilistic clustering.

A hash-based co-clustering algorithm for categorical data

Cluster analysis, or clustering, refers to the analysis of the structural organization of a data set. This analysis is performed by grouping together objects of the data that are more similar among themselves than to objects of different groups. The sampled data may be described by numerical features or by a symbolic representation, known as categorical features. These features often require a transformation into numerical data in order to be properly handled by clustering algorithms. The transformation usually assigns a weight for each feature calculated by a measure of importance (i.e., frequency, mutual information). A problem with the weight assignment is that the values are calculated with respect to the whole set of objects and features. This may pose as a problem when a subset of the features have a higher degree of importance to a subset of objects but a lower degree with another subset. One way to deal with such problem is to measure the importance of each subset of features only with respect to a subset of objects. This is known as co-clustering that, similarly to clustering, is the task of finding a subset of objects and features that presents a higher similarity among themselves than to other subsets of objects and features. As one might notice, this task has a higher complexity than the traditional clustering and, if not properly dealt with, may present an scalability issue. In this paper we propose a novel co-clustering technique, called HBLCoClust, with the objective of extracting a set of co-clusters from a categorical data set, without the guarantees of an enumerative algorithm, but with the compromise of scalability. This is done by using a probabilistic clustering algorithm, named Locality Sensitive Hashing, together with the enumerative algorithm named InClose. The experimental results are competitive when applied to labeled categorical data sets and text corpora. Additionally, it is shown that the extracted co-clusters can be of practical use to expert systems such as Recommender Systems and Topic Extraction.

Probabilistic Distance Clustering: A Spatial Clustering Approach for Remote Sensing Imagery

clustering has been widely applied in various applications, especially in remote sensing technology. Clustering the geographical nature of the remote sensing imagery is challenging due to its wide and dense spatial distribution. Renowned clustering algorithms such as k-means and other probabilistic clustering algorithms have been reported in the literature. However, they are not robust to handle such peculiar data distribution. This paper employs probabilistic d - clustering algorithm to cluster the geographical information of the remote sensing imagery. The methodology considers diverse neighborhood connectivity and degree of connectivity to investigate the performance of probabilistic d - clustering algorithm. Experimental investigation demonstrates that probabilistic d - clustering algorithm is better than k - means clustering algorithm in handling remote sensing imagery.

DeCoaD: determining correlations among diseases using protein interaction networks.

BackgroundDisease–disease similarities can be investigated from multiple perspectives. Identifying similar diseases based on the underlying biomolecular interactions can be especially useful, because it may shed light on the common causes of the diseases and therefore may provide clues for possible treatments. Here we introduce DeCoaD, a web-based program that uses a novel method to assign pair-wise similarity scores, called correlations, to genetic diseases.FindingsDeCoaD uses a random walk to model the flow of information in a network within which nodes are either diseases or proteins and links signify either protein–protein interactions or disease–protein associations. For each protein node, the total number of visits by the random walker is called the weight of that node. Using a disease as both the starting and the terminating points of the random walks, a corresponding vector, whose elements are the weights associated with the proteins, can be constructed. The similarity between two diseases is defined as the cosine of the angle between their associated vectors. For a user-specified disease, DeCoaD outputs a list of similar diseases (with their corresponding correlations), and a graphical representation of the disease families that they belong to. Based on a probabilistic clustering algorithm, DeCoaD also outputs the clusters that the disease of interest is a member of, and the corresponding probabilities. The program also provides an interface to run enrichment analysis for the given disease or for any of the clusters that contains it.ConclusionsDeCoaD uses a novel algorithm to suggest non-trivial similarities between diseases with known gene associations, and also clusters the diseases based on their similarity scores. DeCoaD is available at http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/mn/DeCoaD/.