DC-Den

KMeans clustering is a method to partition your observations into k clusters with k centroids that describes the centre of each cluster . When given a new observation, it is part of a cluster if it is closest to the centroid of that cluster. The diagram above illustrates the k-means clustering concept. The KMeans approach starts by deciding the number of clusters you wish. Then you estimate where the centroids of each cluster might be located. The distance of each observation to each centroid is calculated. Then each observation is re-clustered to the closest centroid. For each new cluster, we re-calculate a new centroid by averaging the cluster data by each feature. We repeat this cycle until no further refinement is achieved. Since Excel LAMBDA does not have iterative loops, a recursive approach will be used. Implementing KMeans clustering in LAMBDA With k-means clustering we implement Predict  before  Fit . Predict Predict takes a list of observations array and a list of centr

In the previous article on  Linear Regression , I mentioned Excel's LINEST function. But if you tried using the returned coefficients, you may notice something peculiar. The order of the returned linear coefficients is in the reverse order of the input data. LINEST documents: The equation for the line is: `y = m_1x_1 + m_2x_2 + ... + m_nx_n+ b` if there are multiple ranges of x-values, where the dependent y-values are a function of the independent x-values. The m-values are coefficients corresponding to each x-value, and b is a constant value. Note that y, x, and m can be vectors. The array that the LINEST function returns is `{m_n, m_(n-1), ..., m_1, b}`.  The input is in the order 1st, 2nd, 3rd, ... but the returned coefficients are in the reverse. And if you were to use the coefficients to predict y for a given `x_1, x_2, x_3, ...`, you would either swap the x-s around or the coefficients around. This isn't intuitive. For this reason you should reinvent LINEST . The inten

I will be using some tools to support my data mining functions. I will put them here for your reference. SelectData This function filters a set of data by rows. The default is every 4 in 5 is selected as training data. Every 5th row is for validation data. dcrML.Help.SelectData =LAMBDA(array, selectTrain, [headers], [ratioTrain], [ratioValidate], LET( ratioTrain, IF(ISOMITTED(ratioTrain), 4, ratioTrain), ratioValidate, IF(ISOMITTED(ratioValidate), 1, ratioValidate), selectTrain, IF(ISOMITTED(selectTrain), TRUE, selectTrain), ratioTotal, ratioTrain + ratioValidate, selected, IF(selectTrain, FILTER(array, MOD(ROW(array),ratioTotal) < ratioTrain), FILTER(array, MOD(ROW(array),ratioTotal) >= ratioTrain) ), IF(ISOMITTED(headers), selected, VSTACK(headers, selected) ) ) ) GetHeaders This function is overloaded. If dataHeaders are provided, it returns them. However if none provided, it returns a sequential headers: "F