Types Of Real Datasets Research Articles

Efficient Class of Variance Estimators for Population using Supplementary Information in Stratified Random Sampling

This paper addresses an efficient class of variance estimators for population using stratified random sampling. The suggested class of estimators using supplementary information has been studied in different circumstances. The expressions of bias and mean square error (MSE) of the proposed estimators are derived up to the first degree of approximation. The theoretical comparison of the proposed and considered estimators is also discussed, which shows that the proposed estimators are more efficient than the existing estimators. Theoretical findings are validated by three different types of real data sets and simulation studies. The numerical results of the proposed and existing estimators are compared in terms of mean square error, percentage relative efficiency and diagrams. It is observed that all the proposed estimators outperform the existing estimators. For instance, the traditional unbiased estimator Ozel et.al [6] and other existing estimators. Lastly, appropriate recommendations have been provided for researchers to use these suggested estimators to solve real-world issues.

Semantic labeling of social big media using distributed online robust classification

Semantic labeling for image datasets is of significant importance in a wide range of social media. However, social datasets with massive amounts of data require effective technologies to increase the quality of classification. In this study, we propose a novel online robust classification using distributed learning method, in which the diffusion method is used over adaptive networks in order to the parallelization of the training process. The loss function of the suggested method is derived from the logarithm cosine function, being more suitable for impulsive noises. Also, the convergence of the proposed method is discussed theoretically. The Extensive experimental results show that the proposed method outperforms the other robust state-of-the-art classification algorithms in the presence of various scenarios, such as: free noise synthetic data sets, pure Gaussian noise, mixture of two Gaussian noises, impulse noise, and Alpha–Beta noise which are added to the synthetic data sets. In addition, the experiments were repeated for two different types of real data sets with real noises and outliers, i.e., UCI and 500PX social media data sets. The obtained results over UCI and 500px data sets on social media with real outliers and mislabeled samples confirmed the acceptable performance of the proposed method.

The Type II Topp Leone-Power Lomax Distribution with Analysis in Lifetime Data

In this article, a new distribution, namely the type II Topp Leone-power Lomax (TIITL-PL) distribution, is proposed. Some statistical properties of the proposed distribution are derived including the survival function, hazard rate function, quantile function, moments, moment-generating function, the density function of order statistics, Lorenz curve and Bonferroni curve. The proposed distribution has a special submodel, that is, the type II Topp Leone-Lomax distribution. The maximum likelihood method is employed for model parameter estimation. For application study, the goodness-of-fit study is presented to illustrate the ability of the proposed model to fit various types of real data sets. The result shows that the TIITL-PL model is a flexible alternative to fit and analysis of lifetime data.

On developing an automatic threshold applied to feature selection ensembles

Feature selection ensemble methods are a recent approach aiming at adding diversity in sets of selected features, improving performance and obtaining more robust and stable results. However, using an ensemble introduces the need for an aggregation step to combine all the output methods that confirm the ensemble. Besides, when trying to improve computational efficiency, ranking methods that order all initial features are preferred, and so an additional thresholding step is also mandatory. In this work two different ensemble designs based on ranking methods are described. The main difference between them is the order in which the combination and thresholding steps are performed. In addition, a new automatic threshold based on the combination of three data complexity measures is proposed and compared with traditional thresholding approaches based on retaining a fixed percentage of features. The behavior of these methods was tested, according to the SVM classification accuracy, with satisfactory results, for three different scenarios: synthetic datasets and two types of real datasets (where sample size is much higher than feature size, and where feature size is much higher than sample size).

The Topp-Leone Generated Weibull Distribution: Regression Model, Characterizations and Applications

This paper introduces a new four-parameter lifetime model called the Topp Leone Generated Weibull (TLGW) distribution. This distribution is a generalization of the two parameter Weibull distribution using the genesis of Topp-Leone distribution. We derive many of its structural properties including ordinary and incomplete moments, quantile and generating functions and order statistics. Parameter estimation using maximum likelihood method and simulation results to assess effectiveness of the distribution are discussed. Also, for the first time, we introduce a regression model based on the new distribution. We prove empirically the importance and flexibility of the new model in modeling various types of real data sets.

Fuzzy clustering based on Forest optimization algorithm

Clustering is one of the classification methods for data analysis and it is one of the ways of data analysis, too. There are various methods for fuzzy clustering using optimization algorithms such as genetic algorithm and particle swarm optimization algorithm that were specified. In this paper, the combination of one of the recent optimization algorithms called Forest optimization algorithm and one of the local search methods called gradient method are used to perform fuzzy clustering. The purpose of applying the gradient method is accelerating the convergence of the used optimization algorithm. To apply the proposed method, 4 types of real data sets are used. Cluster validity measures are used to obtain and verify the accuracy of the proposed method (FOFCM). By analyzing and comparing the results of the proposed method with the results of algorithms GGAFCM (fuzzy clustering based on genetic algorithm) and PSOFCM (fuzzy clustering based on particle swarm optimization algorithm), it has been shown that the accuracy of the proposed approach is significantly increased.

The Parameter-less Randomized Gravitational Clustering algorithm with online clusters’ structure characterization

Although clustering is an unsupervised learning approach, most clustering algorithms require the setting of parameters (such as the number of clusters, minimum density or distance threshold) in advance to work properly. Moreover, discovering an appropriate set of clusters is a difficult task since clusters can have any shape, size and density and is harder in the presence of noise. In fact, the presence of noise can deteriorate the results of many of the clustering techniques that are based on the least squares estimate. This paper presents a data clustering algorithm that does not require a parameter setting process [the Parameter-less Randomized Gravitational Clustering algorithm (Pl-Rgc)] and combines it with a mechanism, based in micro-clusters ideas, for representing a cluster as a set of prototypes. In this way, a set of parameter estimation strategies, previously developed for the Randomized Gravitational Clustering (Rgc), are combined with a newly developed stopping criterion, based on the average number of points merged iteration by iteration, to remove the parameter setting of the Rgc algorithm. The performance of the proposed Pl-Rgc algorithm is evaluated experimentally on two types of synthetic data sets: data sets with Gaussian clusters and with non-parametric clusters and two types of real data sets: five classic machine learning classification data sets and one intrusion detection data set. Our results show that the proposed mechanism is able to deal with noise, finds the appropriated number of clusters and finds an appropriated set of cluster prototypes regardless the type of data is working on.

Profiling Moving Objects by Dividing and Clustering Trajectories Spatiotemporally

An object can move with various speeds and arbitrarily changing directions. Given a bounded area where a set of objects moving around, there are some typical moving styles of the objects at different local regions due to the geography nature or other spatiotemporal conditions. Not only the paths that the objects move along, we also want to know how different groups of objects move with various speeds. Therefore, given a set of collected trajectories spreading in a bounded area, we are interested in discovering the typical moving styles in different regions of all the monitored moving objects. These regional typical moving styles are regarded as the profile of the monitored moving objects, which may help reflect the geoinformation of the observed area and the moving behaviors of the observed moving objects. In this paper, we present DivCluST, an approach to finding regional typical moving styles by dividing and clustering the trajectories in consideration of both the spatial and temporal constraints. Different from the existing works that consider only the spatial properties or just the interesting regions of trajectories, DivCluST focuses more on typical movements in local regions of a bounded area and takes the temporal information into account when designing the criteria for trajectory dividing and the distance measurement for adaptive $(k)$-means clustering. Extensive experiments on three types of real data sets with specially designed visualization are presented to show the effectiveness of DivCluST.

Frequent episode mining within the latest time windows over event streams

With the wide use of EDGEs (electronic data gathering equipments) such as sensors and RFID (radio frequency identification) devices, unprecedented volumes of event streams have been generated. Mining frequent episodes within the latest time windows over event streams plays a significant role in event monitoring. It helps to generate episode rules, which can reflect the latest change, and predict future events effectively. The paper proposes how to mine MinEpi (minimal occurrence based frequent episode) within the latest time windows. The existing MinEpi mining methods are all Apriori-like, which need to scan data time after time and generate quantities of candidate episodes. This results in high time and space cost. Moreover, Apriori-like methods cannot be applied to event streams. For these problems, the paper proposes the episode matrix and frequent episode tree based mining method (EM&FET), which can generate frequent 2-episodes by constructing an episode matrix and generate higher-level frequent episodes directly by extending lower-level ones gradually, only scanning data once without candidate generation. Moreover, the paper further improves EM&FET, which enhances efficiency and saves space greatly. The experiments on different types of real data sets show the effectiveness and high efficiency of EM&FET and its improvement.

Non-negativity constraints on the pre-image for pattern recognition with kernel machines

Rules of physics in many real-life problems force some constraints to be satisfied. This paper deals with nonlinear pattern recognition under non-negativity constraints. While kernel principal component analysis can be applied for feature extraction or data denoising, in a feature space associated to the considered kernel function, a pre-image technique is required to go back to the input space, e.g., representing a feature in the space of input signals. The main purpose of this paper is to study a constrained pre-image problem with non-negativity constraints. We provide new theoretical results on the pre-image problem, including the weighted combination form of the pre-image, and demonstrate sufficient conditions for the convexity of the problem. The constrained problem is considered with the non-negativity, either on the pre-image itself or on the weights. We propose a simple iterative scheme to incorporate both constraints. A fortuitous side-effect of our method is the sparsity in the representation, a property investigated in this paper. Experimental results are conducted on artificial and real datasets, where many properties are investigated including the sparsity property, and compared to other methods from the literature. The relevance of the proposed method is demonstrated with experimentations on artificial data and on two types of real datasets in signal and image processing.

Genetic algorithm for dyad pattern finding in DNA sequences

In this paper a novel genetic algorithm is presented for the dyad motif finding problem. The genetic algorithm uses a multi-objective fitness function based on the sum of pairs, the number of matches, and the information content. The individuals required for the population pool in the genetic algorithm are optimized by Gibbs sampling method. Also, new crossover and mutation operators are designed. The algorithm is implemented and tested on the different types of real datasets. The results are compared with other well-known algorithms and the effectiveness of our algorithm is shown.

A hybrid neural network and traditional approach for forecasting lumpy demand

Accurate demand forecasting is one of the most key issues in inventory management of spare parts. The problem of modeling future consumption becomes especially difficult for lumpy patterns, which characterized by intervals in which there is no demand and, periods with actual demand occurrences with large variation in demand levels. However, many of the forecasting methods may perform poorly when demand for an item is lumpy. In this study based on the characteristic of lumpy demand patterns of spare parts a hybrid forecasting approach has been developed, which use a multi-layered perceptron neural network and a traditional recursive method for forecasting future demands. In the described approach the multi-layered perceptron are adapted to forecast occurrences of non-zero demands, and then a conventional recursive method is used to estimate the quantity of non-zero demands. In order to evaluate the performance of the proposed approach, their forecasts were compared to those obtained by using Syntetos & Boylan approximation, recently employed multi-layered perceptron neural network, generalized regression neural network and elman recurrent neural network in this area. The models were applied to forecast future demand of spare parts of Arak Petrochemical Company in Iran, using 30 types of real data sets. The results indicate that the forecasts obtained by using our proposed mode are superior to those obtained by using other methods

A hyperplane based indexing technique for high-dimensional data

In this paper, we propose a novel hyperplane based indexing method to support efficient processing of similarity search queries in high-dimensional spaces. The main idea of the proposed index is to improve data partitioning efficiency in a high-dimensional space by using a hyperplane, which further partitions a subspace and can also take advantage of the twin node concept used in the key dimension based index. Compared with the key dimension concept, the hyperplane is more effective in data filtering. High space utilization is achieved by dynamically performing data reallocation between twin nodes. In addition, a post processing step is used after index building to ensure effective filtration. Extensive experiments based on two types of real data sets are conducted and the results illustrate a significantly improved filtering efficiency. Because of the feature of hyperplane, the proposed indexing method is only suitable to Euclidean spaces.