24,150 research outputs found
Machine-Part cell formation through visual decipherable clustering of Self Organizing Map
Machine-part cell formation is used in cellular manufacturing in order to
process a large variety, quality, lower work in process levels, reducing
manufacturing lead-time and customer response time while retaining flexibility
for new products. This paper presents a new and novel approach for obtaining
machine cells and part families. In the cellular manufacturing the fundamental
problem is the formation of part families and machine cells. The present paper
deals with the Self Organising Map (SOM) method an unsupervised learning
algorithm in Artificial Intelligence, and has been used as a visually
decipherable clustering tool of machine-part cell formation. The objective of
the paper is to cluster the binary machine-part matrix through visually
decipherable cluster of SOM color-coding and labelling via the SOM map nodes in
such a way that the part families are processed in that machine cells. The
Umatrix, component plane, principal component projection, scatter plot and
histogram of SOM have been reported in the present work for the successful
visualization of the machine-part cell formation. Computational result with the
proposed algorithm on a set of group technology problems available in the
literature is also presented. The proposed SOM approach produced solutions with
a grouping efficacy that is at least as good as any results earlier reported in
the literature and improved the grouping efficacy for 70% of the problems and
found immensely useful to both industry practitioners and researchers.Comment: 18 pages,3 table, 4 figure
Exploratory Analysis of Functional Data via Clustering and Optimal Segmentation
We propose in this paper an exploratory analysis algorithm for functional
data. The method partitions a set of functions into clusters and represents
each cluster by a simple prototype (e.g., piecewise constant). The total number
of segments in the prototypes, , is chosen by the user and optimally
distributed among the clusters via two dynamic programming algorithms. The
practical relevance of the method is shown on two real world datasets
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Self-Organizing Time Map: An Abstraction of Temporal Multivariate Patterns
This paper adopts and adapts Kohonen's standard Self-Organizing Map (SOM) for
exploratory temporal structure analysis. The Self-Organizing Time Map (SOTM)
implements SOM-type learning to one-dimensional arrays for individual time
units, preserves the orientation with short-term memory and arranges the arrays
in an ascending order of time. The two-dimensional representation of the SOTM
attempts thus twofold topology preservation, where the horizontal direction
preserves time topology and the vertical direction data topology. This enables
discovering the occurrence and exploring the properties of temporal structural
changes in data. For representing qualities and properties of SOTMs, we adapt
measures and visualizations from the standard SOM paradigm, as well as
introduce a measure of temporal structural changes. The functioning of the
SOTM, and its visualizations and quality and property measures, are illustrated
on artificial toy data. The usefulness of the SOTM in a real-world setting is
shown on poverty, welfare and development indicators
A survey of machine learning techniques applied to self organizing cellular networks
In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future
Adaptive Multilevel Cluster Analysis by Self-Organizing Box Maps
Title and table of contents 1
Introduction 3
1\. Cluster Analysis in High-Dimensional Data 7
1.1 Modeling 8
1.2 Problem reduction via representative clustering 13
1.3 Efficient cluster description 16
1.4 How many clusters? 21
2\. Decomposition 23
2.1 General Definition 23
2.2 Approximate box decomposition 25
2.3 Decomposition based representative clustering 27
2.4 Efficient cluster description via approximate box decomposition 34
3\. Adaptive Decomposition by Self-Organized Neural Networks 41
3.1 Self-Organizing Maps (SOM) 42
3.2 Self-Organizing Box Maps (SOBM) 44
3.3 Comparison SOM-SOBM 53
3.4 Computational complexity 56
3.5 Practical extensions 57
4\. Multilevel Representative Clustering 59
4.1 General approach 59
4.2 Adaptive decomposition refinement 60
4.3 Approach based on Perron Cluster analysis 61
5\. Applications 73
5.1 Conformational Analysis of biomolecules 73
5.2 Cluster analysis of insurance customers 87
Conclusion 91
Appendix 93
Symbols 95
Bibliography 97The aim of this thesis is a fruitful combination of Perron Cluster analysis
and self-organized neural networks within an adaptive multilevel clustering
approach that allows a fast and robust identification and an efficient
description of clusters in high-dimensional data. In a general variant that
needs a correct number of clusters k as an input, this new approach is
relevant for a great number of cluster problems since it uses a cluster model
that covers geometrically, but also dynamically based clusters. Its essential
part is a method called representative clustering that guarantees the
applicability to large cluster problems: Based on an adaptive decomposition of
the object space via self-organized neural networks, the original problem is
reduced to a smaller cluster problem. The general clustering approach can be
extended by Perron Cluster analysis so that it can be used for large
reversible dynamic cluster problems, even if a correct number of clusters k is
unknown a priori. The basic application of the extended clustering approach is
the conformational analysis of biomolecules, with great impact in the field of
Drug Design. Here, for the first time the analysis of practically relevant and
large molecules like an HIV protease inhibitor becomes possible.Als Cluster Analyse bezeichnet man den Prozess der Suche und Beschreibung von
Gruppen (Clustern) von Objekten, so daß die Objekte innerhalb eines Clusters
bezüglich eines gegebenen Maßes maximal homogen sind. Die Homogenität der
Objekte hängt dabei direkt oder indirekt von den Ausprägungen ab, die sie für
eine Anzahl festgelegter Attribute besitzen. Die Suche nach Clustern läßt sich
somit als Optimierungsproblem auffassen, wobei die Anzahl der Cluster vorher
bekannt sein muß. Wenn die Anzahl der Objekte und der Attribute groß ist,
spricht man von komplexen, hoch-dimensionalen Cluster Problemen. In diesem
Fall ist eine direkte Optimierung zu aufwendig, und man benötigt entweder
heuristische Optimierungsverfahren oder Methoden zur Reduktion der
Komplexität. In der Vergangenheit wurden in der Forschung fast ausschließlich
Verfahren für geometrisch basierte Clusterprobleme entwickelt. Bei diesen
Problemen lassen sich die Objekte als Punkte in einem von den Attributen
aufgespannten metrischen Raum modellieren; das verwendete Homogenitätsmaß
basiert auf der geometrischen Distanz der den Objekten zugeordneten Punkte.
Insbesondere zur Bestimmung sogenannter metastabiler Cluster sind solche
Verfahren aber offensichtlich nicht geeignet, da metastabile Cluster, die z.B.
in der Konformationsanalyse von Biomolekülen von zentraler Bedeutung sind,
nicht auf einer geometrischen, sondern einer dynamischen Ähnlichkeit beruhen.
In der vorliegenden Arbeit wird ein allgemeines Clustermodell vorgeschlagen,
das zur Modellierung geometrischer, wie auch dynamischer Clusterprobleme
geeignet ist. Es wird eine Methode zur Komplexitätsreduktion von
Clusterproblemen vorgestellt, die auf einer zuvor generierten Komprimierung
der Objekte innerhalb des Datenraumes basiert. Dabei wird bewiesen, daß eine
solche Reduktion die Clusterstruktur nicht zerstört, wenn die Komprimierung
fein genug ist. Mittels selbstorganisierter neuronaler Netze lassen sich
geeignete Komprimierungen berechnen. Um eine signifikante
Komplexitätsreduktion ohne Zerstörung der Clusterstruktur zu erzielen, werden
die genannten Methoden in ein mehrstufiges Verfahren eingebettet. Da neben der
Identifizierung der Cluster auch deren effiziente Beschreibung notwendig ist,
wird ferner eine spezielle Art der Komprimierung vorgestellt, der eine
Boxdiskretisierung des Datenraumes zugrunde liegt. Diese ermöglicht die
einfache Generierung von regelbasierten Clusterbeschreibungen. Für einen
speziellen Typ von Homogenitätsfunktionen, die eine stochastische Eigenschaft
besitzen, wird das mehrstufige Clusterverfahren um eine Perroncluster Analyse
erweitert. Dadurch wird die Anzahl der Cluster, im Gegensatz zu herkömmlichen
Verfahren, nicht mehr als Eingabeparameter benötigt. Mit dem entwickelten
Clusterverfahren kann erstmalig eine computergestützte Konformationsanalyse
großer, für die Praxis relevanter Biomoleküle durchgeführt werden. Am Beispiel
des HIV Protease Inhibitors VX-478 wird dies detailliert beschrieben
- …