Pythagoras0: /* 노트 */ 새 문단

2020-12-21T10:00:50Z

노트: 새 문단

새 문서

== 노트 ==

===위키데이터===
* ID : [https://www.wikidata.org/wiki/Q622825 Q622825]
===말뭉치===
# Cluster analysis aims at the detection of natural partitioning of objects.<ref name="ref_9eb2b77a">[https://www.sciencedirect.com/topics/medicine-and-dentistry/cluster-analysis Cluster Analysis - an overview]</ref>
# A distance function is used to assess if the similarity between objects and a wide variety of clustering algorithms based on different concepts is available.<ref name="ref_9eb2b77a" />
# Additionally, several merging strategies that lead to different clustering patterns are possible.<ref name="ref_9eb2b77a" />
# Clustering results are therefore somewhat subjective, as they greatly depend on the users’ choices.<ref name="ref_9eb2b77a" />
# Cluster analysis is an inductive exploratory technique in the sense that it uncovers structures without explaining the reasons for their existence.<ref name="ref_a16dd888">[https://www.britannica.com/topic/cluster-analysis Cluster analysis | statistics]</ref>
# The choice of a distance type is crucial for all hierarchical clustering algorithms and depends on the nature of the variables and the expected form of the clusters.<ref name="ref_a16dd888" />
# Cluster analysis deals with separating data into groups whose identities are not known in advance.<ref name="ref_f1b10dbb">[https://www.sciencedirect.com/topics/earth-and-planetary-sciences/cluster-analysis Cluster Analysis - an overview]</ref>
# In modern statistical parlance, cluster analysis is an example of unsupervised learning, whereas discriminant analysis is an instance of supervised learning.<ref name="ref_f1b10dbb" />
# In general, in cluster analysis even the correct number of groups into which the data should be sorted is not known ahead of time.<ref name="ref_f1b10dbb" />
# Gong and Richman (1995) have compared various clustering approaches in a climatological context and catalog the literature with applications of clustering to atmospheric data through 1993.<ref name="ref_f1b10dbb" />
# Cluster analysis, like reduced space analysis (factor analysis), is concerned with data matrices in which the variables have not been partitioned beforehand into criterion versus predictor subsets.<ref name="ref_c4971584">[https://www.qualtrics.com/experience-management/research/cluster-analysis/ Cluster Analysis: Definition and Methods]</ref>
# Cluster analysis is an unsupervised learning algorithm, meaning that you don’t know how many clusters exist in the data before running the model.<ref name="ref_c4971584" />
# Unlike many other statistical methods, cluster analysis is typically used when there is no assumption made about the likely relationships within the data.<ref name="ref_c4971584" />
# If there is a strong clustering effect present, this should be small (more homogenous).<ref name="ref_c4971584" />
# Cluster analysis itself is not one specific algorithm, but the general task to be solved.<ref name="ref_7002c1d8">[https://en.wikipedia.org/wiki/Cluster_analysis Cluster analysis]</ref>
# Clustering can therefore be formulated as a multi-objective optimization problem.<ref name="ref_7002c1d8" />
# Cluster analysis as such is not an automatic task, but an iterative process of knowledge discovery or interactive multi-objective optimization that involves trial and failure.<ref name="ref_7002c1d8" />
# A "clustering" is essentially a set of such clusters, usually containing all objects in the data set.<ref name="ref_7002c1d8" />
# Cluster analysis is a class of techniques that are used to classify objects or cases into relative groups called clusters.<ref name="ref_5f5845c8">[https://www.statisticssolutions.com/directory-of-statistical-analyses-cluster-analysis/ Statistics Solutions]</ref>
# Cluster analysis is also called classification analysis or numerical taxonomy.<ref name="ref_5f5845c8" />
# Cluster Analysis has been used in marketing for various purposes.<ref name="ref_5f5845c8" />
# Segmentation of consumers in cluster analysis is used on the basis of benefits sought from the purchase of the product.<ref name="ref_5f5845c8" />
# Clustering algorithms use the distance in order to separate observations into different groups.<ref name="ref_e0242a21">[https://towardsdatascience.com/the-complete-guide-to-clustering-analysis-10fe13712787 The complete guide to clustering analysis]</ref>
# The so-called k-means clustering is done via the kmeans() function, with the argument centers that corresponds to the number of desired clusters.<ref name="ref_e0242a21" />
# Remind that the difference with the partition by k-means is that for hierarchical clustering, the number of classes is not specified in advance.<ref name="ref_e0242a21" />
# In hierarchical clustering, dendrograms are used to show the sequence of combinations of the clusters.<ref name="ref_e0242a21" />
# Clustering is a broad set of techniques for finding subgroups of observations within a data set.<ref name="ref_da234d09">[https://uc-r.github.io/kmeans_clustering K-means Cluster Analysis · UC Business Analytics R Programming Guide]</ref>
# Clustering allows us to identify which observations are alike, and potentially categorize them therein.<ref name="ref_da234d09" />
# There are many methods to calculate this distance information; the choice of distance measures is a critical step in clustering.<ref name="ref_da234d09" />
# The choice of distance measures is a critical step in clustering.<ref name="ref_da234d09" />
# Gaussian mixture models, useful for clustering, are described in another chapter of the documentation dedicated to mixture models.<ref name="ref_9da71893">[http://scikit-learn.org/stable/modules/clustering.html 2.3. Clustering — scikit-learn 0.23.2 documentation]</ref>
# This updating happens iteratively until convergence, at which point the final exemplars are chosen, and hence the final clustering is given.<ref name="ref_9da71893" />
# Mean Shift¶ MeanShift clustering aims to discover blobs in a smooth density of samples.<ref name="ref_9da71893" />
# Mean Shift clustering on a synthetic 2D datasets with 3 classes.<ref name="ref_9da71893" />
# Clustering can also help marketers discover distinct groups in their customer base.<ref name="ref_ab5aaab6">[https://www.tutorialspoint.com/data_mining/dm_cluster_analysis.htm Cluster Analysis]</ref>
# Clustering also helps in identification of areas of similar land use in an earth observation database.<ref name="ref_ab5aaab6" />
# The clustering algorithm should be capable of detecting clusters of arbitrary shape.<ref name="ref_ab5aaab6" />
# This method locates the clusters by clustering the density function.<ref name="ref_ab5aaab6" />
# Next, it calculates the new center for each cluster as the centroid mean of the clustering variables for each cluster’s new set of observations.<ref name="ref_d6afa728">[https://www.publichealth.columbia.edu/research/population-health-methods/cluster-analysis-using-k-means K-Means Cluster Analysis]</ref>
# Other methods that do not require all variables to be continuous, including some heirarchical clustering methods, have different assumptions and are discussed in the resources list below.<ref name="ref_d6afa728" />
# The choice of clustering variables is also of particular importance.<ref name="ref_d6afa728" />
# Lastly, cluster analysis methods are similar to other data reduction techniques in that they are largely exploratory tools, thus results should be interpreted with caution.<ref name="ref_d6afa728" />
# Cluster analysis is a statistical method used to group similar objects into respective categories.<ref name="ref_4dc7798a">[https://www.alchemer.com/resources/blog/cluster-analysis/ An Introduction to Cluster Analysis]</ref>
# For example, when cluster analysis is performed as part of market research, specific groups can be identified within a population.<ref name="ref_4dc7798a" />
# Marketers commonly use cluster analysis to develop market segments, which allow for better positioning of products and messaging.<ref name="ref_4dc7798a" />
# Insurance companies often leverage cluster analysis if there are a high number of claims in a given region.<ref name="ref_4dc7798a" />
# Specifically, the Mclust( ) function in the mclust package selects the optimal model according to BIC for EM initialized by hierarchical clustering for parameterized Gaussian mixture models.<ref name="ref_7aa8cbbe">[https://www.statmethods.net/advstats/cluster.html Quick-R: Cluster Analysis]</ref>
# Try the clustering exercise in this introduction to machine learning course.<ref name="ref_7aa8cbbe" />
# Cluster analysis refers to algorithms that group similar objects into groups called clusters.<ref name="ref_1e9a60bf">[https://www.displayr.com/what-is-cluster-analysis/ What is Cluster Analysis?]</ref>
# Typically, cluster analysis is performed on a table of raw data, where each row represents an object and the columns represent quantitative characteristic of the objects.<ref name="ref_1e9a60bf" />
# These quantitative characteristics are called clustering variables.<ref name="ref_1e9a60bf" />
# The main output from cluster analysis is a table showing the mean values of each cluster on the clustering variables.<ref name="ref_1e9a60bf" />
# Clustering methods are used to identify groups of similar objects in a multivariate data sets collected from fields such as marketing, bio-medical and geo-spatial.<ref name="ref_d0877fc7">[https://www.datanovia.com/en/blog/types-of-clustering-methods-overview-and-quick-start-r-code/ 5 Amazing Types of Clustering Methods You Should Know]</ref>
# Chapter Clustering Distance Measures Essentials covers the common distance measures used for assessing similarity between observations.<ref name="ref_d0877fc7" />
# Partitioning clustering Partitioning algorithms are clustering techniques that subdivide the data sets into a set of k groups, where k is the number of groups pre-specified by the analyst.<ref name="ref_d0877fc7" />
# There are different types of partitioning clustering methods.<ref name="ref_d0877fc7" />
# Cluster analysis involves applying clustering algorithms with the goal of finding hidden patterns or groupings in a dataset.<ref name="ref_8df1ff37">[https://www.mathworks.com/discovery/cluster-analysis.html Cluster Analysis]</ref>
# Clustering algorithms form groupings in such a way that data within a group (or cluster) have a higher measure of similarity than data in any other cluster.<ref name="ref_8df1ff37" />
# Cluster analysis is a common method for constructing smaller groups (clusters) from a large set of data.<ref name="ref_4d92eaab">[https://www.originlab.com/doc/Origin-Help/Cluster-Analysis Cluster Analysis]</ref>
# Similar to Discriminant Analysis, Cluster analysis is also concerned with classifying observations into groups.<ref name="ref_4d92eaab" />
# Hierarchical Cluster Analysis is the primary statistical method for finding relatively homogeneous clusters of cases based on measured characteristics.<ref name="ref_4d92eaab" />
# Hierarchical Cluster Analysis is the only way to observe how homogeneous groups of variables are formed.<ref name="ref_4d92eaab" />
# The goal of cluster analysis in marketing is to accurately segment customers in order to achieve more effective customer marketing via personalization.<ref name="ref_321c241f">[https://www.optimove.com/resources/learning-center/customer-segmentation-via-cluster-analysis Customer Clustering: Cluster Segmentation Analysis]</ref>
# A common cluster analysis method is a mathematical algorithm known as k-means cluster analysis, sometimes referred to as scientific segmentation.<ref name="ref_321c241f" />
# The following chart shows the results of a three-dimension cluster analysis performed on the customer base of an e-commerce site.<ref name="ref_321c241f" />
# Cluster analysis is an unsupervised learning technique that groups a set of unlabeled objects into clusters that are more similar to each other than the data in other clusters.<ref name="ref_8b0484e7">[https://deepai.org/machine-learning-glossary-and-terms/cluster-analysis Cluster Analysis]</ref>
# Cluster analysis is not so much a single algorithm as it is a process of many subordinate functions, such as discriminant analysis.<ref name="ref_8b0484e7" />
# We will cover K-means and Hierarchical clustering techniques, which are two simple, yet widely used, cluster analysis methods.<ref name="ref_e60e6dfd">[https://www.edx.org/course/cluster-analysis Cluster Analysis]</ref>
# Another remarkable observation in some patterns was the clustering of diseases of the same system or the presence of diseases, reflecting a complication.<ref name="ref_98ae67fc">[https://bmcfampract.biomedcentral.com/articles/10.1186/s12875-018-0790-x Multimorbidity patterns with K-means nonhierarchical cluster analysis]</ref>
# If there is already a field on Color, Tableau moves that field to Detail and replaces it on Color with the clustering results.<ref name="ref_f7bd1485">[https://help.tableau.com/current/pro/desktop/en-us/clustering.htm Find Clusters in Data]</ref>
# Clustering is available in Tableau Desktop, but is not available for authoring on the web (Tableau Server, Tableau Online).<ref name="ref_f7bd1485" />
# So if you rename the saved cluster group, that renaming is not applied to the original clustering in the view.<ref name="ref_f7bd1485" />
# When the measures in the view are disaggregated and the measures you are using as clustering variables are not the same as the measures in the view.<ref name="ref_f7bd1485" />
# Cluster analysis is a statistical technique that has been used extensively by the marketing profession to identify like segments of a target buying population for a particular product.<ref name="ref_0678f9bd">[https://theactuarymagazine.org/cluster-analysis/ The Actuary Magazine]</ref>
# Agglomerative hierarchical clustering is a process that begins by defining one cluster for each record in a particular data set or population.<ref name="ref_0678f9bd" />
# On the other hand, distance clustering starts with a “seed” for each of the maximum number of clusters as defined by the user.<ref name="ref_0678f9bd" />
# The approach used for the VA example presented in this article falls into this second category of cluster analysis methods and is called K-mean Euclidean Distance Method.<ref name="ref_0678f9bd" />
# The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists.<ref name="ref_ee29a564">[https://www.pnas.org/content/95/25/14863 Cluster analysis and display of genome-wide expression patterns]</ref>
# Clustering methods can be divided into two general classes, designated supervised and unsupervised clustering (4).<ref name="ref_ee29a564" />
# In supervised clustering, vectors are classified with respect to known reference vectors.<ref name="ref_ee29a564" />
# In unsupervised clustering, no predefined reference vectors are used.<ref name="ref_ee29a564" />
# Several approaches have been developed or are in development to harness the implied power in this data, and one of them is known as “cluster analysis”.<ref name="ref_eaa4d8a8">[https://www.earley.com/blog/cluster-analysis-big-data-mining-explained-without-math Cluster Analysis in Big Data Mining Explained - Without the Math]</ref>
# The second “probabilistic” clustering method, also known as “soft assignment”, bases analyses on the spatial probability of data points and outliers.<ref name="ref_eaa4d8a8" />
# Another major issue with clustering big data is dimensionality.<ref name="ref_eaa4d8a8" />
# Partitioning and grid based clustering are two methods which can help handle very high dimensional data.<ref name="ref_eaa4d8a8" />
# You can also use cluster analysis to summarize data rather than to find "natural" or "real" clusters; this use of clustering is sometimes called dissection.<ref name="ref_d65b686b">[https://support.sas.com/rnd/app/stat/procedures/ClusterAnalysis.html STAT Cluster Analysis Procedures]</ref>
# The FASTCLUS procedure performs a disjoint cluster analysis on the basis of distances computed from one or more quantitative variables.<ref name="ref_d65b686b" />
# Hierarchical cluster analysis to identify the homogeneous desertification management units.<ref name="ref_ddb7087d">[https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0226355 Hierarchical cluster analysis to identify the homogeneous desertification management units]</ref>
# It is possible to recognize groups having comparable environmental features by applying the clustering method.<ref name="ref_ddb7087d" />
# In this study, we propose using cluster analysis in different working units after determining the desertification intensity map to identify the units that require the same management decisions.<ref name="ref_ddb7087d" />
# Cluster analysis is a significant technique for classifying a ‘mountain’ of information into manageable, meaningful piles.<ref name="ref_ddb7087d" />
# Introduction Cluster analysis is a way of “slicing and dicing” data to allow the grouping together of similar entities and the separation of dissimilar ones.<ref name="ref_1debde78">[https://cran.r-project.org/web/packages/diceR/vignettes/overview.html Cluster Analysis using diceR]</ref>
# Issues arise due to the existence of a diverse number of clustering algorithms, each with different techniques and inputs, and with no universally optimal methodology.<ref name="ref_1debde78" />
# Thus, a framework for cluster analysis and validation methods are needed.<ref name="ref_1debde78" />
# We have currently implemented about 15 clustering algorithms, and we provide a simple framework to add additional algorithms (see example("consensus_cluster") ).<ref name="ref_1debde78" />
# The book is organized according to the traditional core approaches to cluster analysis, from the origins to recent developments.<ref name="ref_d4c0b1d5">[https://www.routledge.com/Handbook-of-Cluster-Analysis/Hennig-Meila-Murtagh-Rocci/p/book/9780367570408 Handbook of Cluster Analysis]</ref>
# After an overview of approaches and a quick journey through the history of cluster analysis, the book focuses on the four major approaches to cluster analysis.<ref name="ref_d4c0b1d5" />
# This handbook is accessible to readers from various disciplines, reflecting the interdisciplinary nature of cluster analysis.<ref name="ref_d4c0b1d5" />
# For those already experienced with cluster analysis, the book offers a broad and structured overview.<ref name="ref_d4c0b1d5" />
# The Wolfram Language has broad support for non-hierarchical and hierarchical cluster analysis, allowing data that is similar to be clustered together.<ref name="ref_e4091c5e">[https://reference.wolfram.com/language/guide/ClusterAnalysis.html Cluster Analysis—Wolfram Language Documentation]</ref>
# This paper investigates and compares the use of a number of existing clustering methods for traffic pattern identifications, considering the above.<ref name="ref_a134d28f">[https://www.hindawi.com/journals/jat/2019/1628417/ Pattern Recognition Using Clustering Analysis to Support Transportation System Management, Operations, and Modeling]</ref>
# A large proportion of the conducting clustering analysis to support the applications mentioned above have utilized the K-means clustering method.<ref name="ref_a134d28f" />
# The goal of the study is to support transportation agencies in their selection of a clustering technique and associated parameters for identifying operational scenarios.<ref name="ref_a134d28f" />
# This paper investigates and demonstrates the use of a number of existing clustering methods for traffic pattern identifications.<ref name="ref_a134d28f" />
# The method of identifying similar groups of data in a dataset is called clustering.<ref name="ref_3f6d74de">[https://www.analyticsvidhya.com/blog/2016/11/an-introduction-to-clustering-and-different-methods-of-clustering/ Clustering Applications]</ref>
# Now, that we understand what is clustering.<ref name="ref_3f6d74de" />
# In hard clustering, each data point either belongs to a cluster completely or not.<ref name="ref_3f6d74de" />
# Soft Clustering: In soft clustering, instead of putting each data point into a separate cluster, a probability or likelihood of that data point to be in those clusters is assigned.<ref name="ref_3f6d74de" />
# Definition - What does Cluster Analysis mean?<ref name="ref_621ee527">[https://www.techopedia.com/definition/30391/cluster-analysis Definition from Techopedia]</ref>
# Cluster analysis comprises a range of methods for classifying multivariate data into subgroups.<ref name="ref_b675cf94">[https://www.wiley.com/en-us/Cluster+Analysis%2C+5th+Edition-p-9780470749913 Cluster Analysis, 5th Edition]</ref>
# By organizing multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present.<ref name="ref_b675cf94" />
# Practitioners and researchers working in cluster analysis and data analysis will benefit from this book.<ref name="ref_b675cf94" />
# The results are stored as named clustering vectors in a list object.<ref name="ref_1f51e77b">[http://girke.bioinformatics.ucr.edu/GEN242/pages/mydoc/Rclustering.html Cluster Analysis in R]</ref>
# Then a nested sapply loop is used to generate a similarity matrix of Jaccard Indices for the clustering results.<ref name="ref_1f51e77b" />
# (ML) algorithms are tasked with, somewhere along the line, you’ll be using clustering techniques quite liberally.<ref name="ref_be3a93a6">[https://www.explorium.ai/blog/clustering-when-you-should-use-it-and-avoid-it/ ML Clustering: When To Use Cluster Analysis And When To Avoid It l Explorium]</ref>
# More importantly, clustering is an easy way to perform many surface-level analyses that can give you quick wins in a variety of fields.<ref name="ref_be3a93a6" />
# Marketers can perform a cluster analysis to quickly segment customer demographics, for instance.<ref name="ref_be3a93a6" />
# Even so, it would be a shame to leave your analysis at clustering, since it’s not meant to be a single answer to your questions.<ref name="ref_be3a93a6" />
# It’s important to understand how cluster analysis differs from other approaches.<ref name="ref_6776b41f">[https://improvado.io/blog/cluster-analysis-for-marketers-the-ultimate-guide Cluster Analysis for Marketers: The Ultimate Guide]</ref>
# While with two variables clustering analysis might seem easy and intuitive, this is not the case when you start adding customer attributes.<ref name="ref_6776b41f" />
# It should happen iteratively by following one of the several clustering algorithms available.<ref name="ref_6776b41f" />
# To prepare for clustering, you'll need to have granular level data for each customer, each product, etc.<ref name="ref_6776b41f" />
# Fuzzy clustering, a method already recognized in many disciplines, provides a more flexible alternative to these traditional clustering methods.<ref name="ref_761e8219">[https://www.frontiersin.org/articles/10.3389/fpsyg.2014.00343/full Applications of cluster analysis to the creation of perfectionism profiles: a comparison of two clustering approaches]</ref>
# Fuzzy clustering differs from other traditional clustering methods in that it allows for a case to belong to multiple clusters simultaneously.<ref name="ref_761e8219" />
# Unfortunately, fuzzy clustering techniques remain relatively unused in the social and behavioral sciences.<ref name="ref_761e8219" />
# The purpose of this paper is to introduce fuzzy clustering to these audiences who are currently relatively unfamiliar with the technique.<ref name="ref_761e8219" />
===소스===
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2021년 2월 17일 (수) 08:12에 Pythagoras0님의 편집

Pythagoras0: /* 메타데이터 */ 새 문단

Pythagoras0: /* 노트 */ 새 문단

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		+	* ID : [https://www.wikidata.org/wiki/Q622825 Q622825]