The algorithm I choose to implement for this project was K-means clustering. The data generated attempted to model a water distribution scenario based on the distance each point is from a proposed water-well. Essentially, the algorithm is ideal for a customer segmentation scenario that clusters customers around a particular water-well based on their location and _k _numbers of wells.
In this imaginary scenario, several local municipalities have struggled for years with its aging water infrastructure. Supply, storage, movement of water from one point to another are a challenge. A data scientist was called in to build some models about how customers will be affected and to shed some light on improving water delivery supply.
The goal was to understand the algorithm; however, when variables have real meaning it helps us to get under the hood to see how it works and the math behind it.
Example Problem: How will customers cluster with respect to their location? Create a visual model for the placement of 3 and 7 water-wells, each well serving as the centroid.
The location of customers were fixed equidistance points, generated using the built-in range function. There were a total of 1392 points.
Image by Author
After plotting the fixed location of each customer, I found what I considered to be the geographical center of the dataset. This I assumed would be the ideal place for the first water-well.
#water #data-science #python #lambda-school #k-means-clustering
This article provides an overview of core data science algorithms used in statistical data analysis, specifically k-means and k-medoids clustering.
Clustering is one of the major techniques used for statistical data analysis.
As the term suggests, “clustering” is defined as the process of gathering similar objects into different groups or distribution of datasets into subsets with a defined distance measure.
K-means clustering is touted as a foundational algorithm every data scientist ought to have in their toolbox. The popularity of the algorithm in the data science industry is due to its extraordinary features:
#big data #big data analytics #k-means clustering #big data algorithms #k-means #data science algorithms
K-means is one of the simplest unsupervised machine learning algorithms that solve the well-known data clustering problem. Clustering is one of the most common data analysis tasks used to get an intuition about data structure. It is defined as finding the subgroups in the data such that each data points in different clusters are very different. We are trying to find the homogeneous subgroups within the data. Each group’s data points are similarly based on similarity metrics like a Euclidean-based distance or correlation-based distance.
The algorithm can do clustering analysis based on features or samples. We try to find the subcategory of sampling based on attributes or try to find the subcategory of parts based on samples. The practical applications of such a procedure are many: the best use of clustering in amazon and Netflix recommended system, given a medical image of a group of cells, a clustering algorithm could aid in identifying the centers of the cells; looking at the GPS data of a user’s mobile device, their more frequently visited locations within a certain radius can be revealed; for any set of unlabeled observations, clustering helps establish the existence of some structure of data that might indicate that the data is separable.
K-means the clustering algorithm whose primary goal is to group similar elements or data points into a cluster.
K in k-means represents the number of clusters.
A cluster refers to a collection of data points aggregated together because of certain similarities.
K-means clustering is an iterative algorithm that starts with k random numbers used as mean values to define clusters. Data points belong to the group represented by the mean value to which they are closest. This mean value co-ordinates called the centroid.
Iteratively, the mean value of each cluster’s data points is computed, and the new mean values are used to restart the process till the mean stops changing. The disadvantage of k-means is that it a local search procedure and could miss global patterns.
The k initial centroids can be randomly selected. Another approach of determining k is to compute the entire dataset’s mean and add _k _random co-ordinates to it to make k initial points. Another method is to determine the principal component of the data and divide it into _k _equal partitions. The mean of each section can be used as initial centroids.
#data-science #algorithms #clustering #k-means #machine-learning
SciPy is the most efficient open-source library in python. The main purpose is to compute mathematical and scientific problems. There are many sub-packages in SciPy which further increases its functionality. This is a very important package for data interpretation. We can segregate clusters from the data set. We can perform clustering using a single or multi-cluster. Initially, we generate the data set. Then we perform clustering on the data set. Let us learn more SciPy Clusters.
It is a method that can employ to determine clusters and their center. We can use this process on the raw data set. We can define a cluster when the points inside the cluster have the minimum distance when we compare it to points outside the cluster. The k-means method operates in two steps, given an initial set of k-centers,
The process iterates until the center value becomes constant. We then fix and assign the center value. The implementation of this process is very accurate using the SciPy library.
#numpy tutorials #clustering in scipy #k-means clustering in scipy #scipy clusters #numpy
Clustering comes under the data mining topic and there is a lot of research going on in this field and there exist many clustering algorithms.
The following are the main types of clustering algorithms.
Following are some of the applications of clustering
#machine-learning #k-means-clustering #clustering #k-means
I consider myself an active StackOverflow user, despite my activity tends to vary depending on my daily workload. I enjoy answering questions with angular tag and I always try to create some working example to prove correctness of my answers.
To create angular demo I usually use either plunker or stackblitz or even jsfiddle. I like all of them but when I run into some errors I want to have a little bit more usable tool to undestand what’s going on.
Many people who ask questions on stackoverflow don’t want to isolate the problem and prepare minimal reproduction so they usually post all code to their questions on SO. They also tend to be not accurate and make a lot of mistakes in template syntax. To not waste a lot of time investigating where the error comes from I tried to create a tool that will help me to quickly find what causes the problem.
Angular demo runner Online angular editor for building demo. ng-run.com <>
Let me show what I mean…
There are template parser errors that can be easy catched by stackblitz
It gives me some information but I want the error to be highlighted
#mean stack #angular 6 passport authentication #authentication in mean stack #full stack authentication #mean stack example application #mean stack login and registration angular 8 #mean stack login and registration angular 9 #mean stack tutorial #mean stack tutorial 2019 #passport.js