A Comparison of Clustering and Prediction Methods for Identifying Key Chemical–Biological Features Affecting Bioreactor Performance
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Text Mining Course for KNIME Analytics Platform
Text Mining Course for KNIME Analytics Platform KNIME AG Copyright © 2018 KNIME AG Table of Contents 1. The Open Analytics Platform 2. The Text Processing Extension 3. Importing Text 4. Enrichment 5. Preprocessing 6. Transformation 7. Classification 8. Visualization 9. Clustering 10. Supplementary Workflows Licensed under a Creative Commons Attribution- ® Copyright © 2018 KNIME AG 2 Noncommercial-Share Alike license 1 https://creativecommons.org/licenses/by-nc-sa/4.0/ Overview KNIME Analytics Platform Licensed under a Creative Commons Attribution- ® Copyright © 2018 KNIME AG 3 Noncommercial-Share Alike license 1 https://creativecommons.org/licenses/by-nc-sa/4.0/ What is KNIME Analytics Platform? • A tool for data analysis, manipulation, visualization, and reporting • Based on the graphical programming paradigm • Provides a diverse array of extensions: • Text Mining • Network Mining • Cheminformatics • Many integrations, such as Java, R, Python, Weka, H2O, etc. Licensed under a Creative Commons Attribution- ® Copyright © 2018 KNIME AG 4 Noncommercial-Share Alike license 2 https://creativecommons.org/licenses/by-nc-sa/4.0/ Visual KNIME Workflows NODES perform tasks on data Not Configured Configured Outputs Inputs Executed Status Error Nodes are combined to create WORKFLOWS Licensed under a Creative Commons Attribution- ® Copyright © 2018 KNIME AG 5 Noncommercial-Share Alike license 3 https://creativecommons.org/licenses/by-nc-sa/4.0/ Data Access • Databases • MySQL, MS SQL Server, PostgreSQL • any JDBC (Oracle, DB2, …) • Files • CSV, txt -
A Survey of Clustering with Deep Learning: from the Perspective of Network Architecture
Received May 12, 2018, accepted July 2, 2018, date of publication July 17, 2018, date of current version August 7, 2018. Digital Object Identifier 10.1109/ACCESS.2018.2855437 A Survey of Clustering With Deep Learning: From the Perspective of Network Architecture ERXUE MIN , XIFENG GUO, QIANG LIU , (Member, IEEE), GEN ZHANG , JIANJING CUI, AND JUN LONG College of Computer, National University of Defense Technology, Changsha 410073, China Corresponding authors: Erxue Min ([email protected]) and Qiang Liu ([email protected]) This work was supported by the National Natural Science Foundation of China under Grant 60970034, Grant 61105050, Grant 61702539, and Grant 6097003. ABSTRACT Clustering is a fundamental problem in many data-driven application domains, and clustering performance highly depends on the quality of data representation. Hence, linear or non-linear feature transformations have been extensively used to learn a better data representation for clustering. In recent years, a lot of works focused on using deep neural networks to learn a clustering-friendly representation, resulting in a significant increase of clustering performance. In this paper, we give a systematic survey of clustering with deep learning in views of architecture. Specifically, we first introduce the preliminary knowledge for better understanding of this field. Then, a taxonomy of clustering with deep learning is proposed and some representative methods are introduced. Finally, we propose some interesting future opportunities of clustering with deep learning and give some conclusion remarks. INDEX TERMS Clustering, deep learning, data representation, network architecture. I. INTRODUCTION property of highly non-linear transformation. For the sim- Data clustering is a basic problem in many areas, such as plicity of description, we call clustering methods with deep machine learning, pattern recognition, computer vision, data learning as deep clustering1 in this paper. -
A Tree-Based Dictionary Learning Framework
A Tree-based Dictionary Learning Framework Renato Budinich∗ & Gerlind Plonka† June 11, 2020 We propose a new outline for adaptive dictionary learning methods for sparse encoding based on a hierarchical clustering of the training data. Through recursive application of a clustering method the data is organized into a binary partition tree representing a multiscale structure. The dictionary atoms are defined adaptively based on the data clusters in the partition tree. This approach can be interpreted as a generalization of a discrete Haar wavelet transform. Furthermore, any prior knowledge on the wanted structure of the dictionary elements can be simply incorporated. The computational complex- ity of our proposed algorithm depends on the employed clustering method and on the chosen similarity measure between data points. Thanks to the multi- scale properties of the partition tree, our dictionary is structured: when using Orthogonal Matching Pursuit to reconstruct patches from a natural image, dic- tionary atoms corresponding to nodes being closer to the root node in the tree have a tendency to be used with greater coefficients. Keywords. Multiscale dictionary learning, hierarchical clustering, binary partition tree, gen- eralized adaptive Haar wavelet transform, K-means, orthogonal matching pursuit 1 Introduction arXiv:1909.03267v2 [cs.LG] 9 Jun 2020 In many applications one is interested in sparsely approximating a set of N n-dimensional data points Y , columns of an n N real matrix Y = (Y1;:::;Y ). Assuming that the data j × N ∗R. Budinich is with the Fraunhofer SCS, Nordostpark 93, 90411 Nürnberg, Germany, e-mail: re- [email protected] †G. Plonka is with the Institute for Numerical and Applied Mathematics, University of Göttingen, Lotzestr. -
Comparison of Similarity Coefficients Used for Cluster Analysis with Dominant Markers in Maize (Zea Mays L)
Genetics and Molecular Biology, 27, 1, 83-91 (2004) Copyright by the Brazilian Society of Genetics. Printed in Brazil www.sbg.org.br Research Article Comparison of similarity coefficients used for cluster analysis with dominant markers in maize (Zea mays L) Andréia da Silva Meyer1, Antonio Augusto Franco Garcia2, Anete Pereira de Souza3 and Cláudio Lopes de Souza Jr.2 1Uscola Superior de Agricultura “Luiz de Queiroz”, Departamento de Ciências Exatas, Piracicaba, SP, Brazil. 2Escola Superior de Agricultura “Luiz de Queiroz”, Departamento de Genética, Piracicaba, SP, Brazil. 3Universidade Estadual de Campinas, Departamento de Genética e Evolução, Campinas, SP, Brazil. Abstract The objective of this study was to evaluate whether different similarity coefficients used with dominant markers can influence the results of cluster analysis, using eighteen inbred lines of maize from two different populations, BR-105 and BR-106. These were analyzed by AFLP and RAPD markers and eight similarity coefficients were calculated: Jaccard, Sorensen-Dice, Anderberg, Ochiai, Simple-matching, Rogers and Tanimoto, Ochiai II and Russel and Rao. The similarity matrices obtained were compared by the Spearman correlation, cluster analysis with dendrograms (UPGMA, WPGMA, Single Linkage, Complete Linkage and Neighbour-Joining methods), the consensus fork index between all pairs of dendrograms, groups obtained through the Tocher optimization procedure and projection efficiency in a two-dimensional space. The results showed that for almost all methodologies and marker systems, the Jaccard, Sorensen-Dice, Anderberg and Ochiai coefficient showed close results, due to the fact that all of them exclude negative co-occurrences. Significant alterations in the results for the Simple Matching, Rogers and Tanimoto, and Ochiai II coefficients were not observed either, probably due to the fact that they all include negative co-occurrences. -
Comparison of Dimensionality Reduction Techniques on Audio Signals
Comparison of Dimensionality Reduction Techniques on Audio Signals Tamás Pál, Dániel T. Várkonyi Eötvös Loránd University, Faculty of Informatics, Department of Data Science and Engineering, Telekom Innovation Laboratories, Budapest, Hungary {evwolcheim, varkonyid}@inf.elte.hu WWW home page: http://t-labs.elte.hu Abstract: Analysis of audio signals is widely used and this work: car horn, dog bark, engine idling, gun shot, and very effective technique in several domains like health- street music [5]. care, transportation, and agriculture. In a general process Related work is presented in Section 2, basic mathe- the output of the feature extraction method results in huge matical notation used is described in Section 3, while the number of relevant features which may be difficult to pro- different methods of the pipeline are briefly presented in cess. The number of features heavily correlates with the Section 4. Section 5 contains data about the evaluation complexity of the following machine learning method. Di- methodology, Section 6 presents the results and conclu- mensionality reduction methods have been used success- sions are formulated in Section 7. fully in recent times in machine learning to reduce com- The goal of this paper is to find a combination of feature plexity and memory usage and improve speed of following extraction and dimensionality reduction methods which ML algorithms. This paper attempts to compare the state can be most efficiently applied to audio data visualization of the art dimensionality reduction techniques as a build- in 2D and preserve inter-class relations the most. ing block of the general process and analyze the usability of these methods in visualizing large audio datasets. -
Robust Hierarchical Clustering∗
Journal of Machine Learning Research 15 (2014) 4011-4051 Submitted 12/13; Published 12/14 Robust Hierarchical Clustering∗ Maria-Florina Balcan [email protected] School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213, USA Yingyu Liang [email protected] Department of Computer Science Princeton University Princeton, NJ 08540, USA Pramod Gupta [email protected] Google, Inc. 1600 Amphitheatre Parkway Mountain View, CA 94043, USA Editor: Sanjoy Dasgupta Abstract One of the most widely used techniques for data clustering is agglomerative clustering. Such algorithms have been long used across many different fields ranging from computational biology to social sciences to computer vision in part because their output is easy to interpret. Unfortunately, it is well known, however, that many of the classic agglomerative clustering algorithms are not robust to noise. In this paper we propose and analyze a new robust algorithm for bottom-up agglomerative clustering. We show that our algorithm can be used to cluster accurately in cases where the data satisfies a number of natural properties and where the traditional agglomerative algorithms fail. We also show how to adapt our algorithm to the inductive setting where our given data is only a small random sample of the entire data set. Experimental evaluations on synthetic and real world data sets show that our algorithm achieves better performance than other hierarchical algorithms in the presence of noise. Keywords: unsupervised learning, clustering, agglomerative algorithms, robustness 1. Introduction Many data mining and machine learning applications ranging from computer vision to biology problems have recently faced an explosion of data. As a consequence it has become increasingly important to develop effective, accurate, robust to noise, fast, and general clustering algorithms, accessible to developers and researchers in a diverse range of areas. -
Space-Time Hierarchical Clustering for Identifying Clusters in Spatiotemporal Point Data
International Journal of Geo-Information Article Space-Time Hierarchical Clustering for Identifying Clusters in Spatiotemporal Point Data David S. Lamb 1,* , Joni Downs 2 and Steven Reader 2 1 Measurement and Research, Department of Educational and Psychological Studies, College of Education, University of South Florida, 4202 E Fowler Ave, Tampa, FL 33620, USA 2 School of Geosciences, University of South Florida, 4202 E Fowler Ave, Tampa, FL 33620, USA; [email protected] (J.D.); [email protected] (S.R.) * Correspondence: [email protected] Received: 2 December 2019; Accepted: 27 January 2020; Published: 1 February 2020 Abstract: Finding clusters of events is an important task in many spatial analyses. Both confirmatory and exploratory methods exist to accomplish this. Traditional statistical techniques are viewed as confirmatory, or observational, in that researchers are confirming an a priori hypothesis. These methods often fail when applied to newer types of data like moving object data and big data. Moving object data incorporates at least three parts: location, time, and attributes. This paper proposes an improved space-time clustering approach that relies on agglomerative hierarchical clustering to identify groupings in movement data. The approach, i.e., space–time hierarchical clustering, incorporates location, time, and attribute information to identify the groups across a nested structure reflective of a hierarchical interpretation of scale. Simulations are used to understand the effects of different parameters, and to compare against existing clustering methodologies. The approach successfully improves on traditional approaches by allowing flexibility to understand both the spatial and temporal components when applied to data. The method is applied to animal tracking data to identify clusters, or hotspots, of activity within the animal’s home range. -
Chapter G03 – Multivariate Methods
g03 – Multivariate Methods Introduction – g03 Chapter g03 – Multivariate Methods 1. Scope of the Chapter This chapter is concerned with methods for studying multivariate data. A multivariate data set consists of several variables recorded on a number of objects or individuals. Multivariate methods can be classified as those that seek to examine the relationships between the variables (e.g., principal components), known as variable-directed methods, and those that seek to examine the relationships between the objects (e.g., cluster analysis), known as individual-directed methods. Multiple regression is not included in this chapter as it involves the relationship of a single variable, known as the response variable, to the other variables in the data set, the explanatory variables. Routines for multiple regression are provided in Chapter g02. 2. Background 2.1. Variable-directed Methods Let the n by p data matrix consist of p variables, x1,x2,...,xp,observedonn objects or individuals. Variable-directed methods seek to examine the linear relationships between the p variables with the aim of reducing the dimensionality of the problem. There are different methods depending on the structure of the problem. Principal component analysis and factor analysis examine the relationships between all the variables. If the individuals are classified into groups then canonical variate analysis examines the between group structure. If the variables can be considered as coming from two sets then canonical correlation analysis examines the relationships between the two sets of variables. All four methods are based on an eigenvalue decomposition or a singular value decomposition (SVD)of an appropriate matrix. The above methods may reduce the dimensionality of the data from the original p variables to a smaller number, k, of derived variables that adequately represent the data. -
Minimising Branch Crossings in Phylogenetic Trees Sung-Hyuk Cha
22 Int. J. Applied Pattern Recognition, Vol. 3, No. 1, 2016 Minimising branch crossings in phylogenetic trees Sung-Hyuk Cha* Department of Computer Science, Pace University, New York, NY, USA Email: [email protected] *Corresponding author Yoo Jung An Engineering Technologies and Computer Sciences, Essex County College, Newark, NJ, USA Email: [email protected] Abstract: While phylogenetic trees are widely used in bioinformatics, one of the major problems is that different dendrograms may be constructed depending on several factors. Albeit numerous quantitative measures to compare two different phylogenetic trees have been proposed, visual comparison is often necessary. Displaying a pair of alternative phylogenetic trees together by finding a proper order of taxa in the leaf level was considered earlier to give better visual insights of how two dendrograms are similar. This approach raised a problem of branch crossing. Here, a couple of efficient methods to count the number of branch crossings in the trees for a given taxa order are presented. Using the number of branch crossings as a fitness function, genetic algorithms are used to find a taxa order such that two alternative phylogenetic trees can be shown with semi-minimal number of branch crossing. A couple of methods to encode/decode a taxa order to/from a chromosome where genetic operators can be applied are also given. Keywords: clustering; dendrogram; hierarchical clustering; phylogenetic tree; visualisation. Reference to this paper should be made as follows: Cha, S-H. and An, Y.J. (2016) ‘Minimising branch crossings in phylogenetic trees’, Int. J. Applied Pattern Recognition, Vol. 3, No. 1, pp.22–37. -
A Study of Hierarchical Clustering Algorithm
International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 3, Number 11 (2013), pp. 1225-1232 © International Research Publications House http://www. irphouse.com /ijict.htm A Study of Hierarchical Clustering Algorithm Yogita Rani¹ and Dr. Harish Rohil2 1Reseach Scholar, Department of Computer Science & Application, CDLU, Sirsa-125055, India. 2Assistant Professor, Department of Computer Science & Application, CDLU, Sirsa-125055, India. Abstract Clustering is the process of grouping the data into classes or clusters, so that objects within a cluster have high similarity in comparison to one another but these objects are very dissimilar to the objects that are in other clusters. Clustering methods are mainly divided into two groups: hierarchical and partitioning methods. Hierarchical clustering combine data objects into clusters, those clusters into larger clusters, and so forth, creating a hierarchy of clusters. In partitioning clustering methods various partitions are constructed and then evaluations of these partitions are performed by some criterion. This paper presents detailed discussion on some improved hierarchical clustering algorithms. In addition to this, author have given some criteria on the basis of which one can also determine the best among these mentioned algorithms. Keywords: Hierarchical clustering; BIRCH; CURE; clusters ;data mining. 1. Introduction Data mining allows us to extract knowledge from our historical data and predict outcomes of our future situations. Clustering is an important data mining task. It can be described as the process of organizing objects into groups whose members are similar in some way. Clustering can also be define as the process of grouping the data into classes or clusters, so that objects within a cluster have high similarity in comparison to one another but are very dissimilar to objects in other clusters. -
Dendrogram, Cladogram and Cluster Analysis
See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/312552467 Dendrogram, Cladogram and Cluster Analysis Chapter · January 2015 CITATIONS READS 0 704 1 author: Jyoti Prasad Gajurel 23 PUBLICATIONS 93 CITATIONS SEE PROFILE Some of the authors of this publication are also working on these related projects: Bryoflora of Nepal View project Flora of Nepal View project All content following this page was uploaded by Jyoti Prasad Gajurel on 20 April 2019. The user has requested enhancement of the downloaded file. Dendrogram, Cladogram and Cluster Analysis Jyoti Prasad Gajurel Central Department of Botany, Tribhuvan University, Kirtipur, Kathmandu, Nepal. Email: [email protected] 1.Introduction A phylogenetic analysis starts with a careful analysis of number and choice of character in the taxa, coding of characters in taxa, making of data matrix and then analyzing the data and interpreting the results (http:// www.ucmp.berkeley.edu/IB181/VPL/Phylo/Phylo3.html). Cladistics help in finding of the branching pattern of the evolution while phenetics classify the overall similarity among taxa without evolutionary studies but the phylogenetic analysis use all the information based on phylogeny (Li, 1993). The method based on share derived characters which is useful in the grouping of the taxa is known as cladistics or phylogenetic systematics (Lipscomb, 1998). The phylogenetic analysis which use the evolutionary history as well as studies to make relation of the taxa and the graphical representation is called as Cladogram or Phylogenetic tree. The simple representation of the relationship between the taxa under the study in the graphical form is called dendrogram. -
CNN Features Are Also Great at Unsupervised Classification
CNN features are also great at unsupervised classification Joris Gu´erin,Olivier Gibaru, St´ephaneThiery, and Eric Nyiri Laboratoire des Sciences de l'Information et des Syst`emes (CNRS UMR 7296) Arts et M´etiersParisTech, Lille, France [email protected] ABSTRACT This paper aims at providing insight on the transferability of deep CNN features to unsupervised problems. We study the impact of different pretrained CNN feature extractors on the problem of image set clustering for object classification as well as fine-grained classification. We propose a rather straightforward pipeline combining deep-feature extraction using a CNN pretrained on ImageNet and a classic clustering algorithm to classify sets of images. This approach is compared to state-of-the-art algorithms in image-clustering and provides better results. These results strengthen the belief that supervised training of deep CNN on large datasets, with a large variability of classes, extracts better features than most carefully designed engineering approaches, even for unsupervised tasks. We also validate our approach on a robotic application, consisting in sorting and storing objects smartly based on clustering. K EYWORDS Transfer learning, Image clustering, Robotics application 1. Introduction In a close future, it is likely to see industrial robots performing tasks requiring to make complex decisions. In this perspective, we have developed an automatic sorting and storing application (see section 1.1.2) consisting in clustering images based on semantic content and storing the objects in boxes accordingly using a serial robot (https://youtu.be/ NpZIwY3H-gE). This application can have various uses in shopfloors (workspaces can be organized before the workday, unsorted goods can be sorted before packaging, ...), which motivated this study of image-set clustering.