Generalized weighted conditional fuzzy clustering

Fuzzy clustering helps to find natural vague boundaries in data. The fuzzy c-means method is one of the most popular clustering methods based on minimization of a criterion function. Among many existing modifications of this method, conditional or context-dependent c-means is the most interesting one. In this method, data vectors are clustered under conditions based on linguistic terms represented by fuzzy sets. This paper introduces a family of generalized weighted conditional fuzzy c-means clustering algorithms. This family include both the well-known fuzzy c-means method and the conditional fuzzy c-means method. Performance of the new clustering algorithm is experimentally compared with fuzzy c-means using synthetic data with outliers and the Box-Jenkins database.     

Generalized Clos networks

Proposed was an extension to the class of multistage Clos networks based on the interstage connection circuit described by the balanced incomplete block designs considered in the combinatorics.     

Efficient multi-view clustering networks

Applied Intelligence - In the last decade, deep learning has made remarkable progress on multi-view clustering (MvC), with existing literature adopting a broad target to guide the network learning...     

Generalized regression model for sequence matching and clustering

Linear relation has been found to be valuable in rule discovery of stocks, such as if stock X goes up a, stock Y will go down b. The traditional linear regression models the linear relation of two sequences faithfully. However, if a user requires clustering of stocks into groups where sequences have high linearity or similarity with each other, it is prohibitively expensive to compare sequences one by one. In this paper, we present generalized regression model (GRM) to match the linearity of multiple sequences at a time. GRM also gives strong heuristic support for graceful and efficient clustering. The experiments on the stocks in the NASDAQ market mined interesting clusters of stock trends efficiently. Hansheng Lei received his BE from Ocean University of China in 1998, MS from the University of Science and Technology of China in 2001, and Ph.D. from the University at Buffalo, the State University of New York in February 2006, all in computer science. He is currently an assistant professor in CS/CIS Department, University of Texas at Brownsville. His research interests include biometrics, pattern recognition, machine learning, and data mining. Venu Govindaraju is a professor of Computer Science and Engineering at the University at Buffalo (UB), State University of New York. He received his B.-Tech. (Honors) from the Indian Institute of Technology (IIT), Kharagpur, India in 1986, and his Ph.D. degree in Computer Science from UB in 1992. His research is focused on pattern recognition applications in the areas of biometrics and digital libraries.     

Two-stage clustering via neural networks

This paper presents a two-stage approach that is effective for performing fast clustering. First, a competitive neural network (CNN) that can harmonize mean squared error and information entropy criteria is employed to exploit the substructure in the input data by identifying the local density centers. A Gravitation neural network (GNN) then takes the locations of these centers as initial weight vectors and undergoes an unsupervised update process to group the centers into clusters. Each node (called gravi-node) in the GNN is associated with a finite attraction radius and would be attracted to a nearby centroid simultaneously during the update process, creating the Gravitation-like behavior without incurring complicated computations. This update process iterates until convergence and the converged centroid corresponds to a cluster. Compared to other clustering methods, the proposed clustering scheme is free of initialization problem and does not need to pre-specify the number of clusters. The two-stage approach is computationally efficient and has great flexibility in implementation. A fully parallel hardware implementation is very possible.     

Kohonen自组织特征映射模型的推广

基于拓扑特征保持的观点,对Kohonen自组织特征映射模型进行了推广和理论分析.实验结果表明,采用推广后的模型,能够使自组织特征映射更好地保持特征空间的拓扑性质,从而达到更好的应用效果.     

Behavior pattern clustering in blockchain networks

Multimedia Tools and Applications - Blockchain holds promise for being the revolutionary technology, which has the potential to find applications in numerous fields such as digital money, clearing,...     

Distributed data clustering in sensor networks

Low overhead analysis of large distributed data sets is necessary for current data centers and for future sensor networks. In such systems, each node holds some data value, e.g., a local sensor read, and a concise picture of the global system state needs to be obtained. In resource-constrained environments like sensor networks, this needs to be done without collecting all the data at any location, i.e., in a distributed manner. To this end, we address the distributed clustering problem, in which numerous interconnected nodes compute a clustering of their data, i.e., partition these values into multiple clusters, and describe each cluster concisely. We present a generic algorithm that solves the distributed clustering problem and may be implemented in various topologies, using different clustering types. For example, the generic algorithm can be instantiated to cluster values according to distance, targeting the same problem as the famous k-means clustering algorithm. However, the distance criterion is often not sufficient to provide good clustering results. We present an instantiation of the generic algorithm that describes the values as a Gaussian Mixture (a set of weighted normal distributions), and uses machine learning tools for clustering decisions. Simulations show the robustness, speed and scalability of this algorithm. We prove that any implementation of the generic algorithm converges over any connected topology, clustering criterion and cluster representation, in fully asynchronous settings.     

Multi-level clustering protocol for load-balanced and scalable clustering in large-scale wireless sensor networks

The Journal of Supercomputing - The advent of wireless sensor networks (WSNs) has revolutionized the field of smart applications. In order to improve the performance of WSNs, refinement of...     

复杂网络交叠团模糊分析与信息挖掘*

针对复杂网络交叠团的聚类与模糊分析方法设计问题,给出一种新的模糊度量及相应的模糊聚类方法,并以新度量为基础,设计出两种挖掘网络模糊拓扑特征的新指标：团间连接紧密程度和模糊点对交叠团的连接贡献度,并将其用于网络交叠模块拓扑结构宏观分析和团间关键点提取。实验结果表明,使用该聚类与分析方法不仅可以获得模糊团结构,而且能够揭示出新的网络特征。该方法为复杂网络聚类后分析提供了新的视角。     

Generalized picture distance measure and applications to picture fuzzy clustering

Picture fuzzy set (PFS), which is a generalization of traditional fuzzy set and intuitionistic fuzzy set, shows great promises of better adaptation to many practical problems in pattern recognition, artificial life, robotic, expert and knowledge-based systems than existing types of fuzzy sets. An emerging research trend in PFS is development of clustering algorithms which can exploit and investigate hidden knowledge from a mass of datasets. Distance measure is one of the most important tools in clustering that determine the degree of relationship between two objects. In this paper, we propose a generalized picture distance measure and integrate it to a novel hierarchical picture fuzzy clustering method called Hierarchical Picture Clustering (HPC). Experimental results show that the clustering quality of the proposed algorithm is better than those of the relevant ones.     

Constructive feedforward ART clustering networks. I

Part I of this paper proposes a definition of the adaptive resonance theory (ART) class of constructive unsupervised on-line learning clustering networks. Class ART generalizes several well-known clustering models, e.g., ART 1, improved ART 1, adaptive Hamming net (AHN), and Fuzzy ART, which are optimized in terms of memory storage and/or computation time. Next, the symmetric Fuzzy ART (S-Fuzzy ART) network is presented as a possible improvement over Fuzzy ART. As a generalization of S-Fuzzy ART, the simplified adaptive resonance theory (SART) group of ART algorithms is defined. Gaussian ART (GART), which is found in the literature, is presented as one more instance of class SART. In Part II of this work, a novel SART network, called fully self-organizing SART (FOSART), is proposed and compared with Fuzzy ART, S-Fuzzy ART, GART and other well-known clustering algorithms. Results of our comparison may easily extend to the ARTMAP supervised learning framework.     

一种能量高效的无线传感器网络分簇路由算法

无线传感器网络中节点的能量有限,提高能量的有效性便成为无线传感器网络路由协议设计的首要目标。设计了一种能量高效的分簇路由算法,它提出让候选节点在一定的覆盖范围内以剩余能量为标准来竞选簇头,以使簇头分布均匀;处于簇类交界的节点则根据能量和距离来选择归属的簇头,以平衡网络负载;新算法还采用多跳的簇间通信方式来降低大部分簇头节点的通信负载。仿真结果表明:新算法能够有效降低网络能耗,延长网络生存时间。     

Incremental K-clique clustering in dynamic social networks

Clustering entities into dense parts is an important issue in social network analysis. Real social networks usually evolve over time and it remains a problem to efficiently cluster dynamic social networks. In this paper, a dynamic social network is modeled as an initial graph with an infinite change stream, called change stream model, which naturally eliminates the parameter setting problem of snapshot graph model. Based on the change stream model, the incremental version of a well known k-clique clustering problem is studied and incremental k-clique clustering algorithms are proposed based on local DFS (depth first search) forest updating technique. It is theoretically proved that the proposed algorithms outperform corresponding static ones and incremental spectral clustering algorithm in terms of time complexity. The practical performances of our algorithms are extensively evaluated and compared with the baseline algorithms on ENRON and DBLP datasets. Experimental results show that incremental k-clique clustering algorithms are much more efficient than corresponding static ones, and have no accumulating errors that incremental spectral clustering algorithm has and can capture the evolving details of the clusters that snapshot graph model based algorithms miss.     

无线传感器网络中一种有效的分布式簇划分算法

提出了一种快速有效的分布式簇划分算法，为每个节点设定一个初始时间，最先到期的节点成为簇头。考虑到簇头选举的合理性，时间衰减与节点连通度相关，并辅以随机化的方法消除时间同步对算法的影响。通过仿真验证该簇划分算法的有效性，并定量分析了通信半径与平均簇头个数的关系。     

Multi objective clustering for wireless sensor networks

Ambient Intelligence is the next wave in computing and communication technology. Nano-sensors, wireless networks and unified intelligent software are the main elements of this issue. Inputs of ambient intelligence are taken from sensors in the environment. Wireless sensor networks consists of small and low cost sensors that collect and report environmental data. Wireless sensors are dispersed in an area that some phenomenon or event should be monitored. When a sensor detects the monitored event (heat, pressure, sound, light, areas having magnetic properties, vibration, etc.), the event is reported to one of the sites. This site performs an appropriate task such as sending a message or local processing based on the type of network, and then provides the appropriate response. One of the major challenges in wireless sensor networks is optimizing the energy consumption. Studies have shown that by clustering network nodes, it is possible to use their energy more efficiently. This study proposes a clustering based routing method to be used in wireless sensor networks. Multi-objective optimization algorithm named as Non-dominated sorting genetic algorithm-II is used for clustering and seven objective functions are described. It is aimed to carry out several goals at once by using multi objective algorithm. While communication cost between the objective functions and cluster-head and Sink, and cluster-head and non-cluster-head is tried to be minimized, selection of the cluster heads only from the nodes near Sink is also tried to be prevented and it is also taken into consideration for clusters to include more nodes as much as possible. Each solution of the solution set obtained with Non-dominated sorting genetic algorithm-II points a different network topology. Each solution in solution set is the best solution according to some of objective functions. It is provided that Sink simulate each solution in solution set according to a certain scenario and choose one suitable for the desired criteria. In proposed method, both operating the Non-dominated sorting genetic algorithm-II and, simulation and evaluation of the obtained solutions comes out in Sink which has sufficient operation and power sources. According to the results, the proposed method can make the life span of network five times longer than LEACH, which is the most famous clustering algorithm. Besides, while the proposed method extends the life span of network, it is also seen that it increases the number of the packet reaching Sink two times more than LEACH. The data provided by proposed method includes information about larger areas when compared to LEACH.     

Constructive feedforward ART clustering networks. II

For pt.I see ibid., p.645-61 (2002). Part I of this paper defines the class of constructive unsupervised on-line learning simplified adaptive resonance theory (SART) clustering networks. Proposed instances of class SART are the symmetric fuzzy ART (S-Fuzzy ART) and the Gaussian ART (GART) network. In Part II of our work, a third network belonging to class SART, termed fully self-organizing SART (FOSART), is presented and discussed. FOSART is a constructive, soft-to-hard competitive, topology-preserving, minimum-distance-to-means clustering algorithm capable of: 1) generating processing units and lateral connections on an example-driven basis and 2) removing processing units and lateral connections on a minibatch basis. FOSART is compared with Fuzzy ART, S-Fuzzy ART, GART and other well-known clustering techniques (e.g., neural gas and self-organizing map) in several unsupervised learning tasks, such as vector quantization, perceptual grouping and 3-D surface reconstruction. These experiments prove that when compared with other unsupervised learning networks, FOSART provides an interesting balance between easy user interaction, performance accuracy, efficiency, robustness, and flexibility     

无线传感器网络中一种能量均衡的分簇策略

以无线传感器网络中的能量消耗模型为基础,提出了一种能量均衡的无线传感器网络分簇路由协议EECHS(energy-effficient cluster-head selection)。该协议通过节点的剩余能量和节点距离基站的距离来调节其成为簇首的概率,并进一步调节簇的大小。仿真结果表明,与改进后的DCHS协议相比,该策略使网络的生命周期和稳定周期分别提高了31%和45%以上。     

一种异构传感器网络下的节能分簇路由算法

无线传感器网络存在着严重的能量约束,传统同构的传感网络路由协议和算法不适合异构网络,因此,设计异构传感网络下的节能路由算法具有现实意义。研究两种不同类型传感器节点构成的,具有不同的初始能量和不同感知数据能力的异构网络中基于簇头预测的节能分簇路由算法ECAH。根据簇内节点的剩余能量、能量消耗速率和跟上一轮簇头的距离预测出下一轮簇头,有效地减少了控制报文数量,降低了系统开销,节约了能量。仿真结果显示,在异构的网络中采用ECAH路由算法比LEACH算法网络生存时间大约提高了23%。     

Energy-efficient clustering in lossy wireless sensor networks

Recent experimental studies have revealed that a large percentage of wireless links are lossy and unreliable for data delivery in wireless sensor networks (WSNs). Such findings raise new challenges for the design of clustering algorithms in WSNs in terms of data reliability and energy efficiency. In this paper, we propose distributed clustering algorithms for lossy WSNs with a mobile collector, where the mobile collector moves close to each cluster head to receive data directly and then uploads collected data to the base station. We first consider constructing one-hop clusters in lossy WSNs where all cluster members are within the direct communication range of their cluster heads. We formulate the problem into an integer program, aiming at maximizing the network lifetime, which is defined as the number of rounds of data collection until the first node dies. We then prove that the problem is NP-hard. After that, we propose a metric-based distributed clustering algorithm to solve the problem. We adopt a metric called selection weight for each sensor node that indicates both link qualities around the node and its capability of being a cluster head. We further extend the algorithm to multi-hop clustering to achieve better scalability. We have found out that the performance of the one-hop clustering algorithm in small WSNs is very close to the optimal results obtained by mathematical tools. We have conducted extensive simulations for large WSNs and the results demonstrate that the proposed clustering algorithms can significantly improve the data reception ratio, reduce the total energy consumption in the network and prolong network lifetime compared to a typical distributed clustering algorithm, HEED, that does not consider lossy links.