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When clustering incomplete datasets, data on cluster border (border data) are more likely to be misclassified. Aiming at this problem, the proposed algorithm focuses on the re-classification of “suspected misclassified” border data (abbreviated as SM border data). Based on the preliminary clustering results of classical FCM-based algorithm for incomplete data and the KNN (k nearest neighbor) principle,...
Nowadays, many organizations collect large volumes of event log data on a daily basis, and the analysis of collected data is a challenging task. For this purpose, data mining methods have been suggested in past research papers, and several data clustering algorithms have been developed formining line patterns from event logs. In this paper, we introduce an open-source tool called LogClusterC which...
Solar power penetration has made the site-specific energy ratings an essential necessity for utilities, independent systems operators and regional transmission organizations. Since, it leads to the reliable and efficient energy production with the increased levels of solar power integration. This study concentrates on the partitional clustering analysis of monthly average insolation period data for...
The granularity partition for functional modules is a fundamental research topic in robot distributed control technology. How to evaluate the module partition scheme with different granularity, and then obtain the optimum scheme is the urgent problem. In this paper, we proposed a novel evaluation strategy for the granularity partition of functional modules in robotic system using RTM as control platform...
Linguistic summarization techniques make it easy to gain insight into large amounts of data by describing the main properties of the data linguistically. In this paper we focus on a specific type of data, namely process data, i.e., event logs that contain information about when some activities were performed for a particular customer case. An event log may contain many different sequences, because...
Privacy preserving techniques have been actively studied on the time-series data in various fields like financial, medical and weather analysis. I focused towards preserving the data through anonymity and generalization, to resist homogeneity attack. First investigate, what's the privacy to be incorporated in the time-series data and after finding the data which needs to be preserved various perturbation...
Clustering dynamic data is a challenge inidentifying and forming groups. This unsupervised learningusually leads to undirected knowledge discovery. The clusterdetection algorithm searches for clusters of data which aresimilar to one another by using similarity measures.Determining the suitable algorithm which can bring theoptimized groups cluster could be an issue. Depending on theparameters and attributes...
The task of community detection in a graph formalizes the intuitive task of grouping together subsets of vertices such that vertices within clusters are connected tighter than those in disparate clusters. This paper approaches community detection in graphs by constructing Markov random walks on the graphs. The mixing properties of the random walk are then used to identify communities. We use coupling...
In this paper, we want to study the informative value of negative links in signed complex networks. For this purpose, we extract and analyze a collection of signed networks representing voting sessions of the European Parliament (EP). We first process some data collected by the Vote Watch Europe Website for the whole 7th term (2009-2014), by considering voting similarities between Members of the EP...
Feature selection is an essential technique used in high dimensional data. Basically, feature selection is focused on removing irrelevant features. But, removing redundant features is also equally important. We propose a novel feature subset selection algorithm based on the idea of consensus clustering. Our algorithm constructs a complete graph on feature space and partitions the graph using various...
The constrained capacity of wireless sensor nodes and harsh, unattended deploy environments make the data collected by sensor nodes usually unreliable. We have proposed a count-min sketch based anomaly detection scheme with the goal of detecting the anomaly data values in WSN. In this paper, we analyze the performance of the proposed scheme thoroughly. We show through experiments with real sensed...
Most existing methods perform the projected partition over gene expression data based on the untrue assumption of independence among genes. To address the problem, we propose two novel projected partition algorithms, PPA and PPA+. The basic idea of PPA is to take the order among genes as the criterion of phenotype structure discovery. Specially, in PPA, no any specific data distribution assumption...
In this paper we introduce a direct approach for Block modeling in multi-relational networks inspired by the Pajek-Approach. In this article the direct Block modeling-method is presented for two-relational networks and evaluated statistically compared to indirect approaches. In addition we apply the method to the "Krackhardt`s High-tech Managers" dataset to show the feasibility of the approach...
Boiler steam temperature system shows non-linear and time-varying, so the accurate modeling of steam temperature system is particularly important. A kind of method of fuzzy identification based on improved GK clustering algorithm (λ-sectional set fuzzy weighted GK clustering) is proposed in connection with the traditional Fuzzy clustering algorithm's defects such as low precision and slow search speed...
In this paper, we present a cluster algorithm which is an improvement of the multi-objective clustering ensemble algorithm (MOCLE), which is denoted as IMOCLE for short. First, we introduce a new clustering objective function to measure the individual difference in the optimization process so as to remain the diversity of the population. Then, a clustering ensemble technique is applied to MOCLE to...
This paper presents a novel approach and interface to interactive image segmentation. Our interface uses sparse and inaccurate boundary cues provided by the user to produce a multi-layer segmentation of the image. Using boundary cues allows our interface to utilize a single ``boundary brush" to produce a multi-layer segmentation, making it appealing for devices with touch screen user interface...
In this paper we study the recovery of block sparse signals and extend conventional approaches in two important directions; one is learning and exploiting intra-block correlation, and the other is generalizing signals' block structure such that the block partition is not needed to be known for recovery. We propose two algorithms based on the framework of block sparse Bayesian learning (bSBL). One...
In this paper we investigate the dynamic traffic relationship characterized by a similarity value from one road point to another in vehicle networks. Due to the regularity of human mobility, traffic exhibits strong correlations in both temporal domain and spatial domain. By exploiting the similarity values, we derive application-specific message update rules for affinity propagation, based on which...
Hybrid evolutionary algorithms are created by suitably combining the good features of two parent evolutionary algorithms, normally provide better solutions than the individual ones. In this paper we have formulated the partitional clustering as an optimization problem and solved it by a newly developed hybrid evolutionary algorithm Immunized PSO. Simulation studies on four benchmark UCI datasets demonstrate...
In this paper, a hybrid control approach for low temperature combustion engines is presented. The identification as well as the controller design are demonstrated. In order to identify piecewise affine models, we propose to use correlation clustering algorithms, which are developed and used in the field of data mining. We outline the identification of the low temperature combustion engine from measurement...
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