Lower Membership Values Research Articles

A Mixed Clustering Approach for Real-Time Anomaly Detection

Anomaly detection in real-time data is accepted as a vital area of research. Clustering techniques have effectively been applied for the detection of anomalies several times. As the datasets are real time, the time of data generation is important. Most of the existing clustering-based methods either follow a partitioning or a hierarchical approach without addressing time attributes of the dataset distinctly. In this article, a mixed clustering approach is introduced for this purpose, which also takes time attributes into consideration. It is a two-phase method that first follows a partitioning approach, then an agglomerative hierarchical approach. The dataset can have mixed attributes. In phase one, a unified metric is used that is defined based on mixed attributes. The same metric is also used for merging similar clusters in phase two. Tracking of the time stamp associated with each data instance is conducted simultaneously, producing clusters with different lifetimes in phase one. Then, in phase two, the similar clusters are merged along with their lifetimes. While merging the similar clusters, the lifetimes of the corresponding clusters with overlapping cores are merged using superimposition operation, producing a fuzzy time interval. This way, each cluster will have an associated fuzzy lifetime. The data instances either belonging to sparse clusters, not belonging to any of the clusters or falling in the fuzzy lifetimes with low membership values can be treated as anomalies. The efficacy of the algorithms can be established using both complexity analysis as well as experimental studies. The experimental results with a real world dataset and a synthetic dataset show that the proposed algorithm can detect the anomalies with 90% and 98% accuracy, respectively.

A type-2 fuzzy community detection model in large-scale social networks considering two-layer graphs

This paper mainly aims to identify communities with different interactions between nodes in complex networks. Community detection algorithms partition vertices into densely-connected components in a complex network. In recent researches, a node is related to multiple aspects of relationships resulting in new challenges in social networks. The two aspects of relationships could be shown as a two-layer graph which comprises two graphs dependent on each other; and each graph shows a specific aspect of the interaction. In this research, a new community detection model is proposed based on the possibilistic c-means clustering model considering two-layer graphs (PCMTL) in order to detect overlapping communities based on the two-layer graphs using both structural and attribute similarities in large-scale social networks. The nodes are assigned to communities by upper and lower membership values that are indicative of the degree of belonging to the communities through type-2 fuzzy membership values, and the suggested values of interval type-2 fuzzy membership determine how a node belongs to a community with regard to two different aspects of interactions in a two-layer graph. Moreover, according to the proposed model, a validity index is introduced to assess the suggested model in comparison to the approach existing in the literature. Ultimately, two artificial and two real large-scale social networks are used to validate the performance of the suggested model.

Multivariable State-Space Recursive Identification Algorithm Based on Evolving Type-2 Neural-Fuzzy Inference System

In this paper, a novel approach for state-space evolving type-2 neural-fuzzy identification of multivariable dynamic systems is proposed. According to adopted methodology, conditions for creating and merging clusters are used to perform the structural adaptation of the neural-fuzzy model. The center and shape of each cluster are estimated, defining all rules in the interval type-2 neural-fuzzy inference system. The degree of uncertainty on the shape of type-2 membership functions is computed through an extended Kalman filter-based learning mechanism. Once the type-2 membership functions (upper and lower membership values) are estimated, the fuzzy Markov parameters are computed from experimental data, and for each incoming information, the parameters of state-space linear models in the consequent proposition of inference system are recursively estimated. The efficiency and applicability of the proposed methodology are demonstrated through experimental results of modeling of an industrial dryer.

Protein secondary structure prediction based on the fuzzy support vector machine with the hyperplane optimization

The prediction of the protein secondary structure is a crucial point in bioinformatics and related fields. In the last years, machine learning methods have become a valuable tool, achieving satisfactory results. However, the prediction accuracy needs to be further ameliorated. This paper proposes a new method based on an improved fuzzy support vector machine (FSVM) for the prediction of the secondary structure of proteins. Unlike traditional methods to set the membership function, it firstly constructs an approximate optimal separating hyperplane by iterating the class centers in the feature space. Then sample points close to this hyperplane are assigned with large membership values, while outliers with small membership values according to the K-nearest neighbor. And some sample points with low membership values are removed, reducing the training time and improving the prediction accuracy. To optimize the prediction results, our method also exploits information on sequence-based structural similarity. We used three databases (e.g. RS126, CB513 and data1199) to test this method, showing the achievement of 94.2%, 93.1%, 96.7% Q3 accuracy and 91.7%, 89.7%, 94.1% SOV values for the three datasets, respectively. Overall, our method results are comparable to or often better than commonly used methods (Magnan & Baldi, 2014; Sheng et al., 2016) for secondary structure prediction.

Fuzzy duocentric community detection model in social networks

The main goal of this paper is to present a clustering model to identify duocentric communities in the complex networks. A duocentric community is built around two central nodes which are as close as possible to other nodes, while the central nodes are connected enough to each other to shape the center of the community. To detect such communities, we develop a new objective function based clustering model. The network's nodes are assigned to the duocentric communities by the type-2 fuzzy numbers which indicate the degrees of belonging to the communities by upper and lower membership values. Generated interval type-2 fuzzy membership values by our proposed model are able to determine how much each node belongs to the both central nodes and how it is shared among communities. Also, the compatible verification index with the proposed model is introduced to evaluate and compare the results of the proposed model with the existing approach in the literature. Finally, the performance of the proposed algorithm is validated by detecting duocentric communities in three artificial networks and two real social networks.

Multi-stage filtering for improving confidence level and determining dominant clusters in clustering algorithms of gene expression data

A drastic improvement in the analysis of gene expression has lead to new discoveries in bioinformatics research. In order to analyse the gene expression data, fuzzy clustering algorithms are widely used. However, the resulting analyses from these specific types of algorithms may lead to confusion in hypotheses with regard to the suggestion of dominant function for genes of interest. Besides that, the current fuzzy clustering algorithms do not conduct a thorough analysis of genes with low membership values. Therefore, we present a novel computational framework called the “multi-stage filtering-Clustering Functional Annotation” (msf-CluFA) for clustering gene expression data. The framework consists of four components: fuzzy c-means clustering (msf-CluFA-0), achieving dominant cluster (msf-CluFA-1), improving confidence level (msf-CluFA-2) and combination of msf-CluFA-0, msf-CluFA-1 and msf-CluFA-2 (msf-CluFA-3). By employing double filtering in msf-CluFA-1 and apriori algorithms in msf-CluFA-2, our new framework is capable of determining the dominant clusters and improving the confidence level of genes with lower membership values by means of which the unknown genes can be predicted.

Fuzzy c-means improvement using relaxed constraints support vector machines

Fuzzy clustering is a widely applied method for extracting the underlying models within data. It has been applied successfully in many real-world applications. Fuzzy c-means is one of the most popular fuzzy clustering methods because it produces reasonable results and its implementation is straightforward. One problem with all fuzzy clustering algorithms such as fuzzy c-means is that some data points which are assigned to some clusters have low membership values. It is possible that many samples may be assigned to a cluster with low-confidence. In this paper, an efficient and noise-aware implementation of support vector machines, namely relaxed constraints support vector machines, is used to solve the mentioned problem and improve the performance of fuzzy c-means algorithm. First, fuzzy c-means partitions data into appropriate clusters. Then, the samples with high membership values in each cluster are selected for training a multi-class relaxed constraints support vector machine classifier. Finally, the class labels of the remaining data points are predicted by the latter classifier. The performance of the proposed clustering method is evaluated by quantitative measures such as cluster entropy and Minkowski scores. Experimental results on real-life data sets show the superiority of the proposed method.

A new fuzzy-based decision algorithm for high-density impulse noise removal

This paper proposes a new efficient fuzzy-based decision algorithm (FBDA) for the restoration of images that are corrupted with high density of impulse noises. FBDA is a fuzzy-based switching median filter in which the filtering is applied only to corrupted pixels in the image while the uncorrupted pixels are left unchanged. The proposed algorithm computes the difference measure for each pixel based on the central pixel (corrupted pixel) in a selected window and then calculates the membership value for each pixel based on the highest difference. The algorithm then eliminates those pixels from the window with very high and very low membership values, which might represent the impulse noises. Median filter is then applied to the remaining pixels in the window to get the restored value for the current pixel position. The proposed algorithm produces excellent results compared to conventional method such as standard median filter (SMF) as well as some advanced techniques such as adaptive median filters (AMF), efficient decision-based algorithm (EDBA), improved efficient decision-based algorithm (IDBA) and boundary discriminative noise detection (BDND) switching median filter. The efficiency of the proposed algorithm is evaluated using different standard images. From experimental analysis, it has been found that FBDA produces better results in terms of both quantitative measures such as PSNR, SSIM, IEF and qualitative measures such as Image Quality Index (IQI).

Neuro-Fuzzy Techniques for Traffic Control

Intersection stage control using Forward Dynamic Programming (FDP) with a sample time of five seconds is already effective on the field. Neuro-fuzzy techniques are proposed here for controlling each light each second. Rules, fuzzyfication and inference are modeled by a neural network. For each signal, the neuro-fuzzy control selects the highest membership value between ’switch on’ and ‘off’ and presents it to a Petri net. For neurofuzzy acceleration of FDP, only controls with low membership values differences are enumerated. Simulations on different intersections show delay reductions with respect to fixed time from 0% to 30% for neuro-fuzzy control and from 15% to 35% for neuro-fuzzy acceleration of FDP.

Hybrid fuzzy-neural systems in handwritten word recognition

Two hybrid fuzzy neural systems are developed and applied to handwritten word recognition. The word recognition system requires a module that assigns character class membership values to segments of images of handwritten words. The module must accurately represent ambiguities between character classes and assign low membership values to a wide variety of noncharacter segments resulting from erroneous segmentations. Each hybrid is a cascaded system. The first stage of both is a self-organizing feature map (SOFM). The second stages map distances into membership values. The third stage of one system is a multilayer perceptron (MLP). The third stage of the other is a bank of Choquet fuzzy integrals (FI). The two systems are compared individually and as a combination to the baseline system. The new systems each perform better than the baseline system. The MLP system slightly outperforms the FI system, but the combination of the two outperforms the individual systems with a small increase in computational cost over the MLP system. Recognition rates of over 92% are achieved with a lexicon set having average size of 100. Experiments were performed on a standard test set from the SUNY/USPS CD-ROM database.