Hadi Saboohi

On Density-Based Data Streams Clustering Algorithms: A Survey

Clustering data streams has drawn lots of attention in the last few years due to their ever-growing presence. Data streams put additional challenges on clustering such as limited time and memory and one pass clustering. Furthermore, discovering clusters with arbitrary shapes is very important in data stream applications. Data streams are infinite and evolving over time, and we do not have any knowledge about the number of clusters. In a data stream environment due to various factors, some noise appears occasionally. Density-based method is a remarkable class in clustering data streams, which has the ability to discover arbitrary shape clusters and to detect noise. Furthermore, it does not need the number of clusters in advance. Due to data stream characteristics, the traditional density-based clustering is not applicable. Recently, a lot of density-based clustering algorithms are extended for data streams. The main idea in these algorithms is using density-based methods in the clustering process and at the same time overcoming the constraints, which are put out by data stream’s nature. The purpose of this paper is to shed light on some algorithms in the literature on density-based clustering over data streams. We not only summarize the main density-based clustering algorithms on data streams, discuss their uniqueness and limitations, but also explain how they address the challenges in clustering data streams. Moreover, we investigate the evaluation metrics used in validating cluster quality and measuring algorithms’ performance. It is hoped that this survey will serve as a steppingstone for researchers studying data streams clustering, particularly density-based algorithms.

Soft-Computing Methodologies for Precipitation Estimation: A Case Study

The current paper presents an investigation of the accuracy of soft-computing techniques in precipitation estimation. The monthly precipitation data from 29 synoptic stations in Serbia from 1946 to 2012 are used as a case study. Despite a number of mathematical functions having been proposed for modeling precipitation estimation, the models still have disadvantages such as being very demanding in terms of calculation time. Soft computing can be used as an alternative to the analytical approach, as it offers advantages such as no required knowledge of internal system parameters, compact solutions for multivariable problems, and fast calculation. Because precipitation prediction is a crucial problem, a process which simulates precipitation with two soft-computing techniques was constructed and presented in this paper, namely, adaptive neurofuzzy inference (ANFIS) and support vector regression (SVR). In the current study, polynomial, linear, and radial basis function (RBF) are applied as the kernel function of the SVR to estimate the probability of precipitation. The performance of the proposed optimizers is confirmed with the simulation results. The SVR results are also compared with the ANFIS results. According to the experimental results, enhanced predictive accuracy and capability of generalization can be achieved with the ANFIS approach compared to SVR estimation. The simulation results verify the effectiveness of the proposed optimization strategies.

Support vector regression methodology for prediction of input displacement of adaptive compliant robotic gripper

The prerequisite for new versatile grippers is the capability to locate and perceive protests in their surroundings. It is realized that automated controllers are profoundly nonlinear frameworks, and a faultless numerical model is hard to get, in this way making it troublesome to control utilizing tried and true procedure. Here, a design of an adaptive compliant gripper is presented. This design of the gripper has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize specific shapes of the grasping objects. Since the conventional control strategy is a very challenging task, soft computing based controllers are considered as potential candidates for such an application. In this study, the polynomial and radial basis function (RBF) are applied as the kernel function of Support Vector Regression (SVR) to estimate and predict optimal inputs displacement of the gripper according to experimental tests and shapes of grasping objects. Instead of minimizing the observed training error, SVR poly and SVR rbf attempt to minimize the generalization error bound so as to achieve generalized performance. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the SVR approach compared to other soft computing methodology.

MuDi-Stream

Density-based method has emerged as a worthwhile class for clustering data streams. Recently, a number of density-based algorithms have been developed for clustering data streams. However, existing density-based data stream clustering algorithms are not without problem. There is a dramatic decrease in the quality of clustering when there is a range in density of data. In this paper, a new method, called the MuDi-Stream, is developed. It is an online-offline algorithm with four main components. In the online phase, it keeps summary information about evolving multi-density data stream in the form of core mini-clusters. The offline phase generates the final clusters using an adapted density-based clustering algorithm. The grid-based method is used as an outlier buffer to handle both noises and multi-density data and yet is used to reduce the merging time of clustering. The algorithm is evaluated on various synthetic and real-world datasets using different quality metrics and further, scalability results are compared. The experimental results show that the proposed method in this study improves clustering quality in multi-density environments.

Determining the joints most strained in an underactuated robotic finger by adaptive neuro-fuzzy methodology

The main purpose of this paper is to determine what joints are most strained in the proposed underactuated finger by adaptive neuro-fuzzy methodology. For this, kinetostatic analysis of the finger structure is established with added torsional springs in every single joint. Since the finger’s grasping forces depend on torsional spring stiffness in the joints, it is preferable to determine which joints have the most influence on grasping forces. Hence, the finger joints experiencing the most strain during the grasping process should be determined. It is desirable to select and analyze a subset of joints that are truly relevant or the most influential to finger grasping forces in order to build a finger model with optimal grasping features. This procedure is called variable selection. In this study, variable selection is modeled using the adaptive neuro-fuzzy inference system (ANFIS). Variable selection using the ANFIS network is performed to determine how the springs implemented in the finger joints affect the output grasping forces. This intelligent algorithm is applied using the Matlab environment and the performance is analyzed. The simulation results presented in this paper show the effectiveness of the developed method.

An automatic subdigraph renovation plan for failure recovery of composite semantic Web services

A Web service-based system never fulfills a user’s goal unless a failure recovery approach exists. It is inevitable that several Web services may either perish or fail before or during transactions. The completion of a composite process relies on the smooth execution of all constituent Web services. A mediator acts as an intermediary between providers and consumers to monitor the execution of these services. If a service fails, the mediator has to recover the whole composite process or else jeopardize achieving the intended goals. The atomic replacement of a perished Web service usually does not apply because the process of locating a matched Web service is unreliable. Even the system cannot depend on the replacement of the dead service with a composite service. In this paper, we propose an automatic renovation plan for failure recovery of composite semantic services based on an approach of subdigraph replacement. A replacement subdigraph is posed in lieu of an original subdigraph, which includes the failed service. The replacement is done in two separate phases, offline and online, to make the recovery faster. The offline phase foresees all possible subdigraphs, pre-calculates them, and ranks several possible replacements. The online phase compensates the unwanted effects and executes the replacement subdigraph in lieu of the original subdigraph. We have evaluated our approach during an experiment and have found that we could recover more than half of the simulated failures. These achievements show a significant improvement compared to current approaches.

A Fast Density-Based Clustering Algorithm for Real-Time Internet of Things Stream

Data streams are continuously generated over time from Internet of Things (IoT) devices. The faster all of this data is analyzed, its hidden trends and patterns discovered, and new strategies created, the faster action can be taken, creating greater value for organizations. Density-based method is a prominent class in clustering data streams. It has the ability to detect arbitrary shape clusters, to handle outlier, and it does not need the number of clusters in advance. Therefore, density-based clustering algorithm is a proper choice for clustering IoT streams. Recently, several density-based algorithms have been proposed for clustering data streams. However, density-based clustering in limited time is still a challenging issue. In this paper, we propose a density-based clustering algorithm for IoT streams. The method has fast processing time to be applicable in real-time application of IoT devices. Experimental results show that the proposed approach obtains high quality results with low computation time on real and synthetic datasets.

Enlarging Test Collections of Composite Semantic Services

There are numerous methods which are proposed to mediate semantic Web services. The mediations of semantic services include their discovery, composition, execution, and monitoring. These are performed on both atomic and composite services. Newly proposed methods are required to be evaluated on a dataset. Despite the existence of atomic test collections of semantic services, the number of publicly available test collections containing composite services is not comparable. In this paper, we propose an approach to enlarge the number of composite services in a test collection. We generate new composites by calculating subdigraphs of available composite services. We evaluated our approach on a number of composite services and we could exponentially enlarge the size of test collections.