Abstract: The main design parameter of WSN (wireless sensor network) is the energy consumption. To compensate this parameter, hierarchical clustering is a technique that assists in extending duration of the networks life by efficiently consuming the energy. This paper focuses on dealing with the WSNs and the FIS (fuzzy interface system) which are deployed to enhance the BEENISH protocol. The node energy, mobility, pause time and density are considered for the selection of CH (cluster head). The simulation outcomes exhibited that the projected system outperforms the traditional system with regard to the energy utilization and number of packets transmitted to sink.
Abstract: Renewable energy is referred to as "clean energy" and common popular support for the use of renewable energy (RE) is to provide electricity with zero carbon dioxide emissions. This study provides useful insight into the European Union (EU) RE, especially, into electricity generation obtained from renewables, and their targets. The objective of this study is to identify groups of European countries, using multivariate statistical analysis and selected indicators. The hierarchical clustering method is used to decide the number of clusters for EU countries. The conducted statistical hierarchical cluster analysis is based on the Ward’s clustering method and squared Euclidean distances. Hierarchical cluster analysis identified eight distinct clusters of European countries. Then, non-hierarchical clustering (k-means) method was applied. Discriminant analysis was used to determine the validity of the results with data normalized by Z score transformation. To explore the relationship between the selected indicators, correlation coefficients were computed. The results of the study reveal the current situation of RE in European Union Member States.
Abstract: Recent advancement in wireless internetworking has presented a number of dynamic routing protocols based on sensor networks. At present, a number of revisions are made based on their energy efficiency, lifetime and mobility. However, to the best of our knowledge no extensive survey of this special type has been prepared. At present, review is needed in this area where cluster-based structures for dynamic wireless networks are to be discussed. In this paper, we examine and compare several aspects and characteristics of some extensively explored hierarchical dynamic clustering protocols in wireless sensor networks. This document also presents a discussion on the future research topics and the challenges of dynamic hierarchical clustering in wireless sensor networks.
Abstract: Clustering is a process of grouping objects and data
into groups of clusters to ensure that data objects from the same
cluster are identical to each other. Clustering algorithms in one of the
area in data mining and it can be classified into partition, hierarchical,
density based and grid based. Therefore, in this paper we do survey
and review four major hierarchical clustering algorithms called
CURE, ROCK, CHAMELEON and BIRCH. The obtained state of
the art of these algorithms will help in eliminating the current
problems as well as deriving more robust and scalable algorithms for
clustering.
Abstract: In this work, we begin with the presentation of the
Tθ family of usual similarity measures concerning multidimensional
binary data. Subsequently, some properties of these measures are
proposed. Finally the impact of the use of different inter-elements
measures on the results of the Agglomerative Hierarchical Clustering
Methods is studied.
Abstract: Recent advances in wireless networking technologies
introduce several energy aware routing protocols in sensor networks.
Such protocols aim to extend the lifetime of network by reducing the
energy consumption of nodes. Many researchers are looking for
certain challenges that are predominant in the grounds of energy
consumption. One such protocol that addresses this energy
consumption issue is ‘Cluster based hierarchical routing protocol’. In
this paper, we intend to discuss some of the major hierarchical
routing protocols adhering towards sensor networks. Furthermore, we
examine and compare several aspects and characteristics of few
widely explored hierarchical clustering protocols, and its operations
in wireless sensor networks (WSN). This paper also presents a
discussion on the future research topics and the challenges of
hierarchical clustering in WSNs.
Abstract: Textual data plays an important role in the modern
world. The possibilities of applying data mining techniques to
uncover hidden information present in large volumes of text
collections is immense. The Growing Self Organizing Map (GSOM)
is a highly successful member of the Self Organising Map family
and has been used as a clustering and visualisation tool across wide
range of disciplines to discover hidden patterns present in the data.
A comprehensive analysis of the GSOM’s capabilities as a text
clustering and visualisation tool has so far not been published. These
functionalities, namely map visualisation capabilities, automatic
cluster identification and hierarchical clustering capabilities are
presented in this paper and are further demonstrated with experiments
on a benchmark text corpus.
Abstract: We consider n individuals described by p standardized variables, represented by points of the surface of the unit hypersphere Sn-1. For a previous choice of n individuals we suppose that the set of observables variables comes from a mixture of bipolar Watson distribution defined on the hypersphere. EM and Dynamic Clusters algorithms are used for identification of such mixture. We obtain estimates of parameters for each Watson component and then a partition of the set of variables into homogeneous groups of variables. Additionally we will present a factor analysis model where unobservable factors are just the maximum likelihood estimators of Watson directional parameters, exactly the first principal component of data matrix associated to each group previously identified. Such alternative model it will yield us to directly interpretable solutions (simple structure), avoiding factors rotations.
Abstract: This study proposes a new recommender system based on the collaborative folksonomy. The purpose of the proposed system is to recommend Internet resources (such as books, articles, documents, pictures, audio and video) to users. The proposed method includes four steps: creating the user profile based on the tags, grouping the similar users into clusters using an agglomerative hierarchical clustering, finding similar resources based on the user-s past collections by using content-based filtering, and recommending similar items to the target user. This study examines the system-s performance for the dataset collected from “del.icio.us," which is a famous social bookmarking website. Experimental results show that the proposed tag-based collaborative and content-based filtering hybridized recommender system is promising and effectiveness in the folksonomy-based bookmarking website.
Abstract: A new technique of topological multi-scale analysis is
introduced. By performing a clustering recursively to build a
hierarchy, and analyzing the co-scale and intra-scale similarities, an
Iterated Function System can be extracted from any data set. The study
of fractals shows that this method is efficient to extract
self-similarities, and can find elegant solutions the inverse problem of
building fractals. The theoretical aspects and practical
implementations are discussed, together with examples of analyses of
simple fractals.
Abstract: Image clustering is a process of grouping images
based on their similarity. The image clustering usually uses the color
component, texture, edge, shape, or mixture of two components, etc.
This research aims to explore image clustering using color
composition. In order to complete this image clustering, three main
components should be considered, which are color space, image
representation (feature extraction), and clustering method itself. We
aim to explore which composition of these factors will produce the
best clustering results by combining various techniques from the
three components. The color spaces use RGB, HSV, and L*a*b*
method. The image representations use Histogram and Gaussian
Mixture Model (GMM), whereas the clustering methods use KMeans
and Agglomerative Hierarchical Clustering algorithm. The
results of the experiment show that GMM representation is better
combined with RGB and L*a*b* color space, whereas Histogram is
better combined with HSV. The experiments also show that K-Means
is better than Agglomerative Hierarchical for images clustering.
Abstract: Biologically human brain processes information in both unimodal and multimodal approaches. In fact, information is progressively abstracted and seamlessly fused. Subsequently, the fusion of multimodal inputs allows a holistic understanding of a problem. The proliferation of technology has exponentially produced various sources of data, which could be likened to being the state of multimodality in human brain. Therefore, this is an inspiration to develop a methodology for exploring multimodal data and further identifying multi-view patterns. Specifically, we propose a brain inspired conceptual model that allows exploration and identification of patterns at different levels of granularity, different types of hierarchies and different types of modalities. A structurally adaptive neural network is deployed to implement the proposed model. Furthermore, the acquisition of multi-view patterns with the proposed model is demonstrated and discussed with some experimental results.
Abstract: Understanding the cell's large-scale organization is an interesting task in computational biology. Thus, protein-protein interactions can reveal important organization and function of the cell. Here, we investigated the correspondence between protein interactions and function for the yeast. We obtained the correlations among the set of proteins. Then these correlations are clustered using both the hierarchical and biclustering methods. The detailed analyses of proteins in each cluster were carried out by making use of their functional annotations. As a result, we found that some functional classes appear together in almost all biclusters. On the other hand, in hierarchical clustering, the dominancy of one functional class is observed. In the light of the clustering data, we have verified some interactions which were not identified as core interactions in DIP and also, we have characterized some functionally unknown proteins according to the interaction data and functional correlation. In brief, from interaction data to function, some correlated results are noticed about the relationship between interaction and function which might give clues about the organization of the proteins, also to predict new interactions and to characterize functions of unknown proteins.
Abstract: Biological data has several characteristics that strongly differentiate it from typical business data. It is much more complex, usually large in size, and continuously changes. Until recently business data has been the main target for discovering trends, patterns or future expectations. However, with the recent rise in biotechnology, the powerful technology that was used for analyzing business data is now being applied to biological data. With the advanced technology at hand, the main trend in biological research is rapidly changing from structural DNA analysis to understanding cellular functions of the DNA sequences. DNA chips are now being used to perform experiments and DNA analysis processes are being used by researchers. Clustering is one of the important processes used for grouping together similar entities. There are many clustering algorithms such as hierarchical clustering, self-organizing maps, K-means clustering and so on. In this paper, we propose a clustering algorithm that imitates the ecosystem taking into account the features of biological data. We implemented the system using an Ant-Colony clustering algorithm. The system decides the number of clusters automatically. The system processes the input biological data, runs the Ant-Colony algorithm, draws the Topic Map, assigns clusters to the genes and displays the output. We tested the algorithm with a test data of 100 to1000 genes and 24 samples and show promising results for applying this algorithm to clustering DNA chip data.
Abstract: In this paper, a new K-means clustering based
approach for identification of voltage control areas is developed.
Voltage control areas are important for efficient reactive power
management in power systems operating under deregulated
environment. Although, voltage control areas are formed using
conventional hierarchical clustering based method, but the present
paper investigate the capability of K-means clustering for the
purpose of forming voltage control areas. The proposed method is
tested and compared for IEEE 14 bus and IEEE 30 bus systems. The
results show that this K-means based method is competing with
conventional hierarchical approach
Abstract: This work presents a neural network model for the
clustering analysis of data based on Self Organizing Maps (SOM).
The model evolves during the training stage towards a hierarchical
structure according to the input requirements. The hierarchical structure
symbolizes a specialization tool that provides refinements of the
classification process. The structure behaves like a single map with
different resolutions depending on the region to analyze. The benefits
and performance of the algorithm are discussed in application to the
Iris dataset, a classical example for pattern recognition.
Abstract: This paper develops a quality estimation method with
the application of fuzzy hierarchical clustering. Quality estimation is
essential to quality control and quality improvement as a precise
estimation can promote a right decision-making in order to help
better quality control. Normally the quality of finished products in
manufacturing system can be differentiated by quality standards. In
the real life situation, the collected data may be vague which is not
easy to be classified and they are usually represented in term of fuzzy
number. To estimate the quality of product presented by fuzzy
number is not easy. In this research, the trapezoidal fuzzy numbers
are collected in manufacturing process and classify the collected data
into different clusters so as to get the estimation. Since normal
hierarchical clustering methods can only be applied for real numbers,
fuzzy hierarchical clustering is selected to handle this problem based
on quality standards.
Abstract: Understanding the cell's large-scale organization is an
interesting task in computational biology. Thus, protein-protein
interactions can reveal important organization and function of the
cell. Here, we investigated the correspondence between protein
interactions and function for the yeast. We obtained the correlations
among the set of proteins. Then these correlations are clustered using
both the hierarchical and biclustering methods. The detailed analyses
of proteins in each cluster were carried out by making use of their
functional annotations. As a result, we found that some functional
classes appear together in almost all biclusters. On the other hand, in
hierarchical clustering, the dominancy of one functional class is
observed. In brief, from interaction data to function, some correlated
results are noticed about the relationship between interaction and
function which might give clues about the organization of the
proteins.
Abstract: Most of fuzzy clustering algorithms have some
discrepancies, e.g. they are not able to detect clusters with convex
shapes, the number of the clusters should be a priori known, they
suffer from numerical problems, like sensitiveness to the
initialization, etc. This paper studies the synergistic combination of
the hierarchical and graph theoretic minimal spanning tree based
clustering algorithm with the partitional Gath-Geva fuzzy clustering
algorithm. The aim of this hybridization is to increase the robustness
and consistency of the clustering results and to decrease the number
of the heuristically defined parameters of these algorithms to
decrease the influence of the user on the clustering results. For the
analysis of the resulted fuzzy clusters a new fuzzy similarity measure
based tool has been presented. The calculated similarities of the
clusters can be used for the hierarchical clustering of the resulted
fuzzy clusters, which information is useful for cluster merging and
for the visualization of the clustering results. As the examples used
for the illustration of the operation of the new algorithm will show,
the proposed algorithm can detect clusters from data with arbitrary
shape and does not suffer from the numerical problems of the
classical Gath-Geva fuzzy clustering algorithm.