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Cluster Analysis

Cluster Analysis
R has an amazing variety of functions for cluster analysis. In this section, I will describe three of the many approaches: hierarchical agglomerative, partitioning, and model based. While there are no best solutions for the problem of determining the number of clusters to extract, several approaches are given below. Data Preparation Prior to clustering data, you may want to remove or estimate missing data and rescale variables for comparability. # Prepare Data mydata <- na.omit(mydata) # listwise deletion of missing mydata <- scale(mydata) # standardize variables Partitioning K-means clustering is the most popular partitioning method. # Determine number of clusters wss <- (nrow(mydata)-1)*sum(apply(mydata,2,var)) for (i in 2:15) wss[i] <- sum(kmeans(mydata, centers=i)$withinss) plot(1:15, wss, type="b", xlab="Number of Clusters", ylab="Within groups sum of squares") A robust version of K-means based on mediods can be invoked by using pam( ) instead of kmeans( ). Hierarchical Agglomerative Related:  R ResourcesRbooks

ChemWiki: The Dynamic Chemistry E-textbook - Chemwiki Factor Analysis This section covers principal components and factor analysis. The later includes both exploratory and confirmatory methods. Principal Components The princomp( ) function produces an unrotated principal component analysis. # Pricipal Components Analysis # entering raw data and extracting PCs # from the correlation matrix fit <- princomp(mydata, cor=TRUE) summary(fit) # print variance accounted for loadings(fit) # pc loadings plot(fit,type="lines") # scree plot fit$scores # the principal components biplot(fit) click to view Use cor=FALSE to base the principal components on the covariance matrix. The principal( ) function in the psych package can be used to extract and rotate principal components. # Varimax Rotated Principal Components # retaining 5 components library(psych) fit <- principal(mydata, nfactors=5, rotate="varimax") fit # print results mydata can be a raw data matrix or a covariance matrix. Exploratory Factor Analysis mydata can be a raw data matrix or a covariance matrix.

Airbnb New User Bookings, Winner’s Interview: 3rd place: Sandro Vega Pons AirBnB New User Bookings was a popular recruiting competition that challenged Kagglers to predict the first country where a new user would book travel. This was the first recruiting competition on Kaggle with scripts enabled. AirBnB encouraged participants to prove their chops through their collaboration and code sharing in addition to their final models. Sandro Vega Pons took 3rd place, ahead of 1,462 other competitors, using an ensemble of GradientBoosting, MLP, a RandomForest, and an ExtraTreesClassifier. The Basics What was your background prior to entering this challenge? I currently work as a postdoctoral researcher at the NeuroInformatics Laboratory, FBK in Trento, Italy. How did you get started competing on Kaggle? I first heard about Kaggle around three years ago, when a colleague showed me the website. Sandro's top 8 finishes What made you decide to enter this competition? Let's Get Technical What preprocessing and supervised learning methods did you use? Fig. 1 Feature Engineering:

'r' tag wiki karthik/wesanderson How To Perform A Logistic Regression In R Logistic regression is a method for fitting a regression curve, y = f(x), when y is a categorical variable. The typical use of this model is predicting y given a set of predictors x. The predictors can be continuous, categorical or a mix of both. The categorical variable y, in general, can assume different values. Logistic regression implementation in R R makes it very easy to fit a logistic regression model. The dataset We’ll be working on the Titanic dataset. The data cleaning process When working with a real dataset we need to take into account the fact that some data might be missing or corrupted, therefore we need to prepare the dataset for our analysis. <- read.csv('train.csv',header=T,na.strings=c("")) Now we need to check for missing values and look how many unique values there are for each variable using the sapply() function which applies the function passed as argument to each column of the dataframe. data <- subset(,select=c(2,3,5,6,7,8,10,12))

D G Rossiter - Publications & Computer Programs Rossiter, DG 2017. Technical note: Processing the Harmonized World Soil Database in R Version 1.4, 10-Aug-2017, 35 pp. Self-published online. Self-Organising Maps for Customer Segmentation using R Self-Organising Maps (SOMs) are an unsupervised data visualisation technique that can be used to visualise high-dimensional data sets in lower (typically 2) dimensional representations. In this post, we examine the use of R to create a SOM for customer segmentation. The figures shown here used use the 2011 Irish Census information for the greater Dublin area as an example data set. This work is based on a talk given to the Dublin R Users group in January 2014. If you are keen to get down to business: The slides from a talk on this subject that I gave to the Dublin R Users group in January 2014 are available here The code for the Dublin Census data example is available for download from here. SOMs were first described by Teuvo Kohonen in Finland in 1982, and Kohonen’s work in this space has made him the most cited Finnish scientist in the world. The SOM Grid SOM visualisation are made up of multiple “nodes”. SOM Heatmaps Typical SOM visualisations are of “heatmaps”. SOM Algorithm SOMs in R

Variable Selection Procedures - The LASSO | Business Forecasting The LASSO (Least Absolute Shrinkage and Selection Operator) is a method of automatic variable selection which can be used to select predictors X* of a target variable Y from a larger set of potential or candidate predictors X. Developed in 1996 by Tibshirani, the LASSO formulates curve fitting as a quadratic programming problem, where the objective function penalizes the absolute size of the regression coefficients, based on the value of a tuning parameter λ. In doing so, the LASSO can drive the coefficients of irrelevant variables to zero, thus performing automatic variable selection. This post features a toy example illustrating tactics in variable selection with the lasso. The post also dicusses the issue of consistency – how we know from a large sample perspective that we are honing in on the true set of predictors when we apply the LASSO. My take is a two-step approach is often best. Toy Example The following Table illustrates something of the power of the lasso. Real World Examples

Learning Path To Start Your Data Science Career | Career In Analytics Marie said it correctly – the most difficult step in any process is the first step! Recently, we launched a list of various analytics trainings being offered across the globe and are still adding more trainings to it to make it more comprehensive. While we get the entire page up and ready for you, I thought let me start putting down ways in which this information would be helpful to people. What better place to start, than to help out the people who need it the most? These resources should make you knowledge ready for your first job in analytics industry Choice of Language:. I strongly believe that the choice of your first language should be a mainstream one! Books to read: To understand power of analytics: These books provide a good overview of how analytics can impact our business decisions and thought process, challenges faced in implementing data based solutions and also its limitations (the last one). Freakonomics by Steven D. Moneyball by Michael Lewis Gearing up on the subject: Related

r - Text clustering with Levenshtein distances