Cluster analysis is the task of partitioning data into subsets of objects according to their mutual "similarity," without using preexisting knowledge such as class labels. [Clustered-standard-errors and/or cluster-samples should be tagged as such; do NOT use the "clustering" tag for them.]

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How to preform 1NN with single centroid per class in SAS?

I've computer a single centroid per class using PROC fastclus in SAS, ...
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Equal Euclidean distance of a single data point to all the Cluster Centers

In K means Clustering, suppose, if there exists equal euclidean distance of a data point to all of its k cluster centers, which cluster the data point will choose to become its member? Is there any ...
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How to use data analysis output (e.g. clustering) in predictive regression?

I performed some data analysis and visualizations on my dataset and found there are likely $k$ clusters present. How can I use this in a predictive regression setting? My first thought is to create a ...
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Is it necessary for a distance measure used in clustering to correspond to some valid vector space?

I have defined an distance measure based on some properties of points. But I'm not even sure that it corresponds to a valid distance in some vector space. Is this a necessary condition for clustering ?...
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277 views

Using mixed variables in two-step cluster analysis

I need to perform cluster analysis on my data set which includes both continuous and categorical type of variables. Having read around, I think K-Means is not a suitable technique for mixed data. Can ...
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786 views

Dimension reduction for sparse matrix for clustering

I'm looking for a Sparse matrix dimension reduction. I already used some feature selection methods like PCA but it doesn't give me good results. I want to apply mixture models for clustering my data. ...
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667 views

test the significance of clusters

Good morning, I am analyzing a dataset composed by 364 subjects and 13 binary variables (0,1 = absence,presence). I am testing possible association (co-presence) of my variables. To do this, I was ...
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210 views

Spectral clustering of graph

I am trying to cluster a graph using spectral clustering. However I am unaware of the number of classes that exist in the data. Would it be a good idea to apply PCA on the adjacency matrix of the ...
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2k views

Do low silhouette widths mean the data has little underlying structure?

I am new to sequence analysis, and I was wondering how you react if the average silhouette widths (ASW) from cluster analyses of Optimal Matching-based dissimilarity matrices are low (around.25). ...
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663 views

Determining Optimal Number of Cluster in Hierarchical Clustering in Consideration of Variance of Data

I'm applying a Hierarchical Agglomerative Clustering (HAC) for grouping my data and I need to determine the number of the cluster automatically. To determine the optimal number of cluster, I obtain ...
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850 views

Does SPSS rescale dendrograms?

A colleague and I have been clustering some data in SPSS (v19) and R (2.15), respectively. Using the same distance metric and agglomeration method, we get identical merge orders/agglomeration ...
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617 views

Clustering time series

I want to create forecasting for a large quantity of time series. Since they are too many, I am thinking on reducing my data by clustering it into to similar groups. However, I am using SPSS modeler ...
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1k views

Does the average distance in K-means have to be monotone decreasing?

I'm implementing the k-means algorithm myself. I don't see any obvious mistake in my code and it seems to work well. However, there's something I don't understand. My algorithm, working on vectors $...
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1k views

Convergence of K-means

I have a clustering algorithm which works iteratively like K-means, but there are some constraints on cluster sizes with lower and upper thresholds. Do you know any convergence proofs of K-means in ...
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2k views

Clustering high-dimensional sparse binary data

I am trying to cluster Facebook users based on their likes. I have two problems: First, since there is no dislike in Facebook all I have is having likes (1) for some items but for the rest of the ...
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1k views

Which type of regression fits better?

I am a newbie in data mining world. I have a general question. I have a data set which has 10 independent variables and one target variable named as category which has 9 values like: 1, 2, 3, 4, 5, 6, ...
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What are pitfalls of bootstrapping on random sample of master data?

Will I obtain seriously biased results if I use bootstrapping on a subsample of a larger dataset? Rather than drawing 100 bootstrap samples from a dataset of 50 million + records, which could hog ...
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25 views

Curve based clustering of multivariate data (time series like data) [duplicate]

Possible Duplicate: Reducing no of variables subsetted based on depth for PCA I have a question, I am trying to apply a method to my research area, which has not been aplied yet, based on ...
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Sparse estimated data recommender system

Premise: For a product and a user, the system has to recommend him/her other non-users related with him/her that are most likely to be interested in that same product. Available data includes: Non-...
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188 views

Shape analysis of an object to create features for pattern recognition

I am currently with a medical imaging project. Just wondering how to measure the shape of a sphere. For example, how to give a measurement that an object is more like a sphere than the other? I know ...
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160 views

Relevance of overall absolute values in covariance analysis of two variables

I am performing K means clustering on a gene expression dataset. I am aware of the fact that the Pearson correlation metric allows to group trends or patterns irrespective of their overall level of ...
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1answer
172 views

Mahout Scability

Do you know any real world examples of how much Mahout can scale? I wonder how much it can scale in collaborative filtering, clustering, and classification ?
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59 views

Clustering with the same number of objects per group

How can I cluster while forcing the final clusters to have more or less the same number of objects? I have just tried kmeans in R, and it does not take this into ...
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124 views

Issues with tritomising and dichotomising variable

I see that the issue of splitting a variable into two categories has been discussed on this forum and I understand pros, cons as well as some ways of performing it. The issue that I am interested and ...
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394 views

In non-negative matrix factorization, does the first N eigenvector have N greatest variance?

I know for PCA, it's true that the first N eigenvectors have N greatest variance. But I'm not sure whether that's also true for NMF(Non-negative Matrix Factorization). For example, this method(...
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How to explain the connection between SVD and clustering?

Is there an intuitive explanation for how SVD is related to co-clustering when performing SVD on a covariance matrix? (i.e. the SVD is performed on the matrix $E[X Y^{\top}]$ where $X \in \mathbb{R}^...
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970 views

How to define a posterior probability of y given x when the model is not probabilistic?

Suppose we have a very simple online k-means where each new data-point is assigned to its nearest center (the mean is updated incrementally). Each center (cluster) is labelled with the most common ...
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Comparing sample profiles

I have some samples that have been treated with various chemical agents. Users score the treatment effect using categorical values/bins (i.e $< 1, 2.5, 8, ... > 100$). Within a range of say 1-...
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Classification on principal components

For my research I am doing classification on the dataset of three variables. I run unsupervised clustering (based on a histogram peak technique of cluster analysis)and the result I evaluated visually ...
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Choosing which data-point to label (active learning)

For an online unsupervised learning algorithm, data-points are learned sequentially. The performance may improve if in addition to the unlabelled data we have some labelled data-points (i.e. semi-...
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838 views

Calculating similarity and clustering question

I have a dataset of about a million companies containing their names, total employees and annual sales. I want to come up with a function that when given the company returns the 5 most similar ...
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910 views

How to cluster standard errors (by country) using SPSS?

I'm trying to obtain robust standard errors as my regression residuals seem to be correlated... My sample data has 8 countries and I would like to cluster the standard errors by these 8 countries ...
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716 views

How to decide whether to reuse old code or reinvent the wheel?

For a long time now, I have been thinking about working with neural networks and genetic algorithms. I have never been able to decide whether it makes sense to start writing my own code, or to reuse ...
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429 views

bag of words in an online configuration, for classification / clustering

I have a set of image documents. I extract text keywords from this images using OCR to represent each image as a bag of words (a vector where each value is the number of occurrence of a word in the ...
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136 views

Which metric use for problem of clustering spatial data of wind direction and speed?

I have two dimensional spatial (x,y - coordinates of meteo stations) data for small region (so I could neglect the shape of earth globe), for each (x,y) I have one observation of wind direction and ...
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Good Analytical Approach for a problem

I'm trying to figure out what kind of analysis will give me the results I'm looking for. I have 4 shops and I'm trying to understand what is the typical (most likely) customer characteristics of ...
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Clustering with constraint size

I want a clustering method which can let me apply constraints on the maximum and the minimum cluster sizes which I can implement. Otherwise suggestions for a combination of existing techniques which I ...
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492 views

Can sub-optimality of various hierarchical clustering methods be assessed or ranked?

Classic agglomerative hierarchical clustering methods are based on a greedy algorithm. This means that they (many of them) are prone to give sub-optimal solutions instead of the global optimum result, ...
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522 views

Spatial clustering with the constraint that all clusters have equal count

I wish to perform a spatial clustering of scattered data that represents geographic locations of individuals in an urban area. Hierarchical clustering seems to work well, and I have successfully done ...
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1k views

Density calculation in K Means clustering

With the K Clusters generated using K Means Clustering, how do we calculate the density of each cluster? Is there any formula for it?
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Does the sign of the adjusted residuals matter in a crosstable?

In a cross table, if the adjusted residual value for a cell is less than -1.96 or greater than 1.96 then we could say that this cell is determinant in the dependency (suppose pearson is 0.03). ...
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Clustering with 3 attributes

Please bear with me because I am very new to data mining. I have a database of 3 attributes: latitude, longitude and temperature. I want to find clusters for the temperature data and I also want to ...
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Why don't dummy variables have the continuous adjacent category problem in cluster analysis?

I know that if we use categorical variables in cluster analysis we would assume that the scale is continuous and we don't have this concept of distance between two adjacent categories. But what is the ...
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Compute probability of a grouping being correct

I have an exemplar grouping of objects (each with their own feature vector) into categories. I am then given a new grouping of compeltely different objects, and Iw would like to compute the ...
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Determine different clusters of 1d data from database

I have a database table of data transfers between different nodes. This is a huge database (with nearly 40 million transfers). One of the attributes is the number of bytes (nbytes) transfers which ...
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163 views

How can I separate each of 100 observations into groups as determined by the data?

I have 3 covariates for 100 observations. How can I separate each of my 100 observations into groups as determined by the data. I was thinking clustering. However, apparently, I need more than 3 ...
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Does preclustering help to build a better predictive model?

For the task of churn modelling I was considering: Compute k clusters for the data Build k models for each cluster individually. The rationale for that is,that there is nothing to prove, that the ...
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2answers
135 views

Is concept of similarity objective?

Imagine following example: We have two pairs of points (i.e. 4 objects in some space) and two similarity measures. According to first similarity measure, objects from first pair are more similar then ...
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1answer
358 views

kMeans - acceptable value for WCSS

Which value for the within-cluster sum of squares points can be accepted regarding a data set of 1000 tuples, 21 attributes (but only 3 are used now)? I have used Euclidean distance is used, and a ...
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147 views

Distance independent approximation of Nearest Neighbor/k-NN.

Nearest neighbor/k-NN for use with Normalized Compression Distance. I wonder if there exist any approximation of NN/k-NN algorithm which work for all distance measures ? I would like to test ...