Mountain Clustering Research Articles

Phytochemical diversity within and among Sardinian populations of the endemic Teucrium marum L. (Lamiaceae) is determined by ecological factors

Wild aromatic plants present high intra- and inter-population chemical polymorphisms which are of great ecological and economic interest; however, the factors influencing the phytochemical diversity of aromatic plants remain still unexplored for many species. Here, Teucrium marum L. (Lamiaceae) has been studied, a plant endemic to the western Mediterranean islands, very well-known from the phytochemical point of view but scarcely investigated regarding the ecological factors that influence its phytochemical diversity within and among populations. The specific aims were to: 1) define the chemical composition variability of its essential oils; 2) determine its inter- and intra-population chemical variability; and 3) evaluate whether the elevation, climatic factors and/or the soil substrate determined the phytochemical variability of T. marum along a gradient from coastal to mountain wild populations on the island of Sardinia (Italy). Fresh T. marum aerial parts were randomly collected from ten individuals in six different localities in Sardinia: three coastal and three mountainous. Dried leaf samples were hydrodistillated using a classical Clevenger apparatus to obtain the corresponding essential oils. The composition of each essential oil was chemically characterised and analyzed by gas chromatography coupled to mass spectrometry. Ninety compounds were identified: among the others, the two compounds that mainly characterised the essential oils of the studied populations, dolichodial and (E)-β-caryophyllene, are of great economic interest. Statistical analyses showed significant differences in phytochemical essential oil composition among and within the studied populations, which clustered following a geographical pattern rather than a simple climatic or edaphic grouping. Taken together the results here shown shed light on the environmental and geographical conditions that determine the chemical variability of essential oils in T. marum, highlighting a clear coastal vs mountain clustering, which has an ecological and economic relevance, especially for the potential utilization of dolichodial as an environmental-friendly insecticide.

Natural Language Processing through the Subtractive Mountain Clustering Algorithm - A Medication Intake Chatbot

In this work, the subtractive mountain clustering algorithm has been adapted to the problem of natural languages processing in view to construct a chatbot that answers questions posed by the user. The implemented algorithm version allosws for the association of a set of words into clusters. After finding the centre of every cluster — the most relevant word, all the others are aggregated according to a defined metric adapted to the language processing realm. All the relevant stored information (necessary to answer the questions) is processed, as well as the questions, by the algorithm. The correct processing of the text enables the chatbot to produce answers that relate to the posed queries. Since we have in view a chatbot to help elder people with medication, to validate the method, we use the package insert of a drug as the available information and formulate associated questions. Errors in medication intake among elderly people are very common. One of the main causes for this is their loss of ability to retain information. The high amount of medicine intake required by the advanced age is another limiting factor. Thence, the design of an interactive aid system, preferably using natural language, to help the older population with medication is in demand. A chatbot based on a subtractive cluster algorithm is the chosen solution.

Self-adaptive algorithm for changing braking energy recuperation of an electric vehicle based on neuro-fuzzy inference system

This paper describes an expert system prototype that changes the brake energy recuperation coefficient of an electric vehicle. Blackboard is used as the basic architectural pattern, which allowed for working with data obtained from various types of knowledge sources. This approach made it possible to coordinate data from various sources, such as the opinions of drivers, information about the route and the condition of electric vehicles. The expert system is based on the adaptive neuro-fuzzy inference system ANFIS, the rules for which are obtained by mountain clustering data from the training set. Values are averaged using the C-means algorithm.

Similarity matching of wafer bin maps for manufacturing intelligence to empower Industry 3.5 for semiconductor manufacturing

Yield improvement is increasingly important as advanced fabrication technologies are complicated and interrelated for semiconductor manufacturing. Wafer bin maps (WBM) present specific failure patterns which provide crucial information to track the process excursions to empower intelligent manufacturing for wafer fabrication. In practice, WBM identification is still subjective relied on domain knowledge and human-eye. As the semiconductor industry continuously migrates for advanced nano technologies, many rare defect patterns are also generated by different pattern, pattern size, noise degree, pattern density, pattern shift, and wafer rotation. Existing studies regarding WBM focus on classification and lack of capability to detect a rare pattern. In order to overcome the shortage of WBM classification, the similar WBMs provide useful information of WBM identification. Following Industry 3.5 as a hybrid strategy between Industry 3.0 and to-be Industry 4.0, this study aims to develop a novel approach to measure the similarity of defect patterns of WBMs to enhance decision quality for fault detection and defect diagnosis effectively and efficiently. In particular, the proposed approach applied a mountain clustering algorithm to enhance the defect features depending on clustering density. Then, Weighted Modified Hausdorff Distance (WMHD) is employed to measure the similarity level. Furthermore, a decision support system embedded the developed algorithms is constructed. An empirical study of WBM clustering was conducted in a fab for validation. The results have shown practical viability of the proposed approach.

Spatial Modification in the Parameters of Mountain Image Clustering Algorithm

Our proposed method used to overcome the drawbacks of computing values parameters in the mountain algorithm to image clustering. All existing clustering algorithms are required values of parameters to starting the clustering process such as these algorithms have a big problem in computing parameters. One of the famous clustering is a mountain algorithm that gives expected number of clusters, we presented in this paper a new modification of mountain clustering called Spatial Modification in the Parameters of Mountain Image Clustering Algorithm. This modification in the spatial information of image by taking a window mask for each center pixel value to compute distance between pixel and neighborhood for estimation the values of parameters ?, ? that gives a potential optimum number of clusters requiring in image segmentation process. Our experiments show ability the proposed algorithm in image brain segmentation with a quality in the large data sets

Blind equalization and automatic modulation classification based on subspace for subcarrier MPSK optical communications

Equalization can compensate channel distortion caused by channel multipath effects, and effectively improve convergent of modulation constellation diagram in optical wireless system. In this paper, the subspace blind equalization algorithm is used to preprocess M-ary phase shift keying (MPSK) subcarrier modulation signal in receiver. Mountain clustering is adopted to get the clustering centers of MPSK modulation constellation diagram, and the modulation order is automatically identified through the k-nearest neighbor (KNN) classifier. The experiment has been done under four different weather conditions. Experimental results show that the convergent of constellation diagram is improved effectively after using the subspace blind equalization algorithm, which means that the accuracy of modulation recognition is increased. The correct recognition rate of 16PSK can be up to 85% in any kind of weather condition which is mentioned in paper. Meanwhile, the correct recognition rate is the highest in cloudy and the lowest in heavy rain condition.

Correlative Density-Based Clustering

Mountains, which heap up by densities of a data set, intuitively reflect the structure of data points. These mountain clustering methods are useful for grouping data points. However, the previous mountain-based clustering suffers from the choice of parameters which are used to compute the density. In this paper, we adopt correlation analysis to determine the density, and propose a new clustering algorithm, called Correlative Density-based Clustering (CDC). The new algorithm computes the density with a modified way and determines the parameters based on the inherent structure of data points. Experiments on artificial datasets and real datasets demonstrate the simplicity and effectiveness of the proposed approach.

Self-Optimal Clustering Technique Using Optimized Threshold Function

This paper presents a self-optimal clustering (SOC) technique which is an advanced version of improved mountain clustering (IMC) technique. The proposed clustering technique is equipped with major changes and modifications in its previous versions of algorithm. SOC is compared with some of the widely used clustering techniques such as K-means, fuzzy C-means, Expectation and Maximization, and K-medoid. Also, the comparison of the proposed technique is shown with IMC and its last updated version. The quantitative and qualitative performances of all these well-known clustering techniques are presented and compared with the aid of case studies and examples on various benchmarked validation indices. SOC has been evaluated via cluster compactness within itself and separation with other clusters. The optimizing factor in the threshold function is computed via interpolation and found to be effective in forming better quality clusters as verified by visual assessment and various standard validation indices like the global silhouette index, partition index, separation index, and Dunn index.

Finding short structural motifs for re-construction of proteins 3D structure

With a view to find useful building blocks (short structural motifs) for reconstruction of 3D structure of proteins, we propose a modified neural gas learning algorithm that we call structural neural gas (SNG) algorithm. The SNG is applied on a benchmark protein data set and its performance is compared with a well known algorithm from the literature (two stage clustering algorithm (TSCA)). The SNG algorithm is found to generate better building blocks compared to TSCA. We have also compared the performance of SNG algorithm with that of a recently reported Incremental Structural Mountain Clustering Method (ISMCM). In general, ISMCM is found to use more building blocks to yield results comparable to that of SNG algorithm. We demonstrate the superiority of SNG over TSCA both in terms of local-fit and global-fit errors using fragments of length five, six, and seven. We also use a graphical means for comparison of the performance of the two algorithms.

규칙 생성 시스템을 위한 새로운 연속 클러스터링 조합

본 논문에서는 수치적 데이터를 이용하여 규칙을 생성하는 시스템에 대해 순차적인 클러스터링 방법을 제안한다. 단일 클러스터링 기법은 방대하고 복잡한 공간 내에서는 원하는 결과를 얻지 못할 수 있다. 이런 문제점을 해결하기 위해 제안된 방법은 서로 다른 클러스터링 기법을 순차적으로 수행하여 장점들은 활용하고 단점들은 보안하는 형태를 제안하였다. Mountain 클러스터링과 Chen 클러스터링을 이용하여 non-parametric 공간에서 자율적으로 클러스터를 구성하였고, global 공간과 local 공간으로 역할을 분담하여 클러스터를 추정한다. 추정된 클러스터들은 신경회로망이나 퍼지 시스템과 같은 지능 시스템의 구조와 초기 파라미터 결정에 활용될 수 있으며, 확장하여 헬스케어와 의료 분야에서의 결정 제공 시스템의 학습에 도움을 줄 수 있다. 제안된 방법을 유용성을 시뮬레이션을 통해 보이고자 한다. In this paper, we propose a new clustering combination based on numerical data driven for rule generation mechanism. In large and complicated space, a clustering method can obtain limited performance results. To overcome the single clustering method problem, hybrid combined methods can solve problem to divided simple cluster estimation. Fundamental structure of the proposed method is combined by mountain clustering and modified Chen clustering to extract detail cluster information in complicated data distribution of non-parametric space. It has automatic rule generation ability with advanced density based operation when intelligent systems including neural networks and fuzzy inference systems can be generated by clustering results. Also, results of the mechanism will be served to information of decision support system to infer the useful knowledge. It can extend to healthcare and medical decision support system to help experts or specialists. We show and explain the usefulness of the proposed method using simulation and results.

Performance evaluation of data clustering techniques using KDD Cup-99 Intrusion detection data set

Intrusion-detection systems aim at detecting attacks against computer systems and networks or, in general, against information systems. A number of techniques are available for intrusion detection. Data mining is the one of the efficient technique among them. Intrusion detection and clustering have forever been hot topics in the area of machine learning. Data clustering is a procedure of putting related data into groups. Clustering procedure clusters the data into groups with the property of inter-group similarity and intra-group dissimilarity. A clustering technique partitions a data-set into several groups such that the likeness within a group is larger than amongst groups. Clustering as an intrusion detection technique has long before proved to be beneficial. This paper evaluate four most representative off-line clustering techniques: k-means clustering, fuzzy c-means clustering, Mountain clustering, and Subtractive clustering. These techniques are implemented and tested against KDD cup-99 data set, which is used as a standard benchmark data set for intrusion detection. Performance and accuracy of the four techniques are presented and compared in this paper. The experimental outcomes obtained by applying these algorithms on KDD cup-99 data set demonstrate that k-means and fuzzy c-means clustering algorithms perform well in terms of accuracy and computation time.

A Fault Types Recognition System of Power Cable Joint by Hilbert–Huang Transform with Fractal Feature Enhancement Through Mountain Clustering

A Fault Types Recognition System of Power Cable Joint by Hilbert–Huang Transform with Fractal Feature Enhancement Through Mountain Clustering

A Modified Mountain Clustering Algorithm based on Hill Valley Function

A modified mountain clustering algorithm based on the hill valley function is proposed. Firstly, the mountain function is constructed on the data space, with estimating the parameter by a correlation self-comparison method, and database’s mountain function values are computed. Secondly, the hill valley function is introduced to partition the data distributed on each peak. If the hill valley function’ value of two datum equal to 0, it means these two datum are on the same mountain and belong to the same cluster, otherwise they are not. Finally, the data in a cluster with maximum mountain function value is selected as the cluster centre of this cluster. The testing of four databases indicate that the proposed clustering algorithm can categorise the data numbers in each cluster and find all the cluster centres exactly, and no need priori parameters and stopping criterion correlating to the database.

Parameter identification of TSK neuro-fuzzy models

In the framework of the TSK neuro-fuzzy model a combination of the two well-known identification methods are employed for parameter estimation of the neuro-fuzzy inference system, namely the series–parallel and the parallel configurations. The presented paper proposes two new possible configurations for identifying the parameters of the TSK neuro-fuzzy model using the combinations of these two existing configurations. One of the proposed configurations constitutes the series–parallel configuration to the premise part and the parallel configuration to the consequent part of the neuro-fuzzy model, termed as PS-P configuration. The second one is composed of the series–parallel configuration to the consequent part and the parallel configuration to the premise part of the neuro-fuzzy model, termed as CS-P configuration. The presented work mainly deals with a comparative study of the proposed configurations and the existing configurations in the context of parameter identification of the TSK neuro-fuzzy model on three different benchmark examples. Moreover, it investigates upper bound of the learning rates, using the Lyapunov stability theorem, to assure the stability and the convergence of the model learning process. Implementation of the modified mountain clustering (MMC) and the cluster validity function yields initial models. To restrict the upper bound during the learning process it also presents a two-phase adaptive learning rate.

Feature-Weighted Mountain Method with Its Application to Color Image Segmentation

In this paper, we propose a feature-weighted mountain clustering method. The proposed method can work well when there are noisy feature variables and could be useful for obtaining initial estimat of cluster centers for other clustering algorithms. Results from color image segmentation illustrate the proposed method actually produces better segmentation than previous methods.

Incremental Mountain Clustering Method to Find Building Blocks for Constructing Structures of Proteins

In this paper we propose an algorithm named Incremental Structural Mountain Clustering Method (ISMCM) with a view to finding a library of building blocks for reconstruction of 3-D structures of proteins/peptides. The building blocks are short structural motifs that are identified based on an estimate of local "density" of 3-D fragments computed using a measure of structural similarity. The structural similarity is computed after the best-molecular-fit alignment of pairs of fragments. The algorithm is tested on two well known benchmark data sets. Following the protocols used by other researchers, for the first data set we reconstruct a set of 71 test peptides (up to first 60 residues) whereas for the second data set we reconstruct all 143 test peptides. The ISMCM algorithm is found to successfully reconstruct the test peptides in terms of both global-fit root-mean-square (RMS) error and local-fit RMS error. The low values of local-fit RMS errors suggest that these building blocks extracted by ISMCM are good quantizers, which can represent nearby fragments quite accurately. To further assess the quality of building blocks we use two alternative graphical ways. We also use Shannon's entropy to show the structural similarity of the clusters found by our algorithm. This is important as building blocks that represent clusters with structurally similar fragments will be very effective in reconstruction. The entropic analysis reveals a very interesting fact that the secondary structure of the central residue of the fragments in a cluster is most strongly conserved (minimum entropy) over the cluster, which might be an indicator that central residue of the structural motif plays a dominant role in local folding.

Misty Mountain clustering: application to fast unsupervised flow cytometry gating

BackgroundThere are many important clustering questions in computational biology for which no satisfactory method exists. Automated clustering algorithms, when applied to large, multidimensional datasets, such as flow cytometry data, prove unsatisfactory in terms of speed, problems with local minima or cluster shape bias. Model-based approaches are restricted by the assumptions of the fitting functions. Furthermore, model based clustering requires serial clustering for all cluster numbers within a user defined interval. The final cluster number is then selected by various criteria. These supervised serial clustering methods are time consuming and frequently different criteria result in different optimal cluster numbers. Various unsupervised heuristic approaches that have been developed such as affinity propagation are too expensive to be applied to datasets on the order of 106 points that are often generated by high throughput experiments.ResultsTo circumvent these limitations, we developed a new, unsupervised density contour clustering algorithm, called Misty Mountain, that is based on percolation theory and that efficiently analyzes large data sets. The approach can be envisioned as a progressive top-down removal of clouds covering a data histogram relief map to identify clusters by the appearance of statistically distinct peaks and ridges. This is a parallel clustering method that finds every cluster after analyzing only once the cross sections of the histogram. The overall run time for the composite steps of the algorithm increases linearly by the number of data points. The clustering of 106 data points in 2D data space takes place within about 15 seconds on a standard laptop PC. Comparison of the performance of this algorithm with other state of the art automated flow cytometry gating methods indicate that Misty Mountain provides substantial improvements in both run time and in the accuracy of cluster assignment.ConclusionsMisty Mountain is fast, unbiased for cluster shape, identifies stable clusters and is robust to noise. It provides a useful, general solution for multidimensional clustering problems. We demonstrate its suitability for automated gating of flow cytometry data.

Infrared weak target segmentation under complicated background based on mountain clustering

With the analysis of the features of infrared image,an infrared weak target segmentation method based on mountain clustering was presented.First,the original image was projected to x and y coordination to construct dataset and used as sample to cluster.Then based on the datasets,the improved mountain clustering algorithm was implemented,and the clustering centers were employed as the seeds in region growth,and at last the final segmentation was acquired.Experimental results show that the proposed method outperforms the traditional approach and it can provide satisfactory segmentation result.

Misty Mountain Clustering: Application to Fast Unsupervised Flow Cytometry Gating

Flow cytometry experiments record, in individual cells, the fluorescent intensity of different fluorophores that correspond to features such as the levels of specific proteins. An assay typically generates a large number (order 106) of data points in a two or higher dimensional space. The grouping of cells (data points) having similar features, which is referred to as gating, is usually done manually by an expert. We developed software that performs efficient unsupervised gating determining the number of clusters, and the points belonging to each cluster. The program analyses the cross-sections of the histogram created from the data points. The method is particularly efficient in the case of large number of data points such as 104-106. The overall run time for the composite steps of the algorithm increases linearly by the number of data points. In our example 1 million data points, shown in the left part of the figure, were analyzed within 6 seconds on a standard laptop PC. The analysis resulted in 20 clusters, shown in the right side of the figure. The code number of the largest cluster is 1, the second largest is 2, etc.View Large Image | View Hi-Res Image | Download PowerPoint Slide

Structural building blocks

We propose a modified version of the mountain clustering method (MCM) to find a library of structural building blocks for the construction of three-dimensional (3-D) structures of proteins. The algorithm decides on building blocks based on a measure of local density of structural patterns. We tested our algorithm on a well-known data set and found it to successfully reconstruct a set of 71 test proteins (up to first 60 residues as done by others) with lower global-fit root mean square (RMS) errors compared to an existing method that inspired our algorithm. The constructed library of building blocks is also evaluated using some other benchmark data set for comparison. Our algorithm achieved good local-fit RMS errors, indicating that these building blocks can model the nearby fragments quite accurately. In this context, we have proposed two alternative ways to compare the quality of such quantization and reconstruction results, which can be used in other applications too.