K Means Clustering is an unsupervised learning algorithm that tries to cluster data based on their similarity. Unsupervised learning means that there is no outcome to be predicted, and the algorithm just tries to find patterns in the data. In'k' means clustering, we have the specify the number of clusters we want the data to be grouped into. The algorithm randomly assigns each observation to a cluster, and finds the centroid of each cluster. These two steps are repeated till the within cluster variation cannot be reduced any further.
A new algorithm is proposed which accelerates the mini-batch k-means algorithm of Sculley (2010) by using the distance bounding approach of Elkan (2003). We argue that, when incorporating distance bounds into a mini-batch algorithm, already used data should preferentially be reused. To this end we propose using nested mini-batches, whereby data in a mini-batch at iteration t is automatically reused at iteration t+1. Using nested mini-batches presents two difficulties. The first is that unbalanced use of data can bias estimates, which we resolve by ensuring that each data sample contributes exactly once to centroids. The second is in choosing mini-batch sizes, which we address by balancing premature fine-tuning of centroids with redundancy induced slow-down. Experiments show that the resulting nmbatch algorithm is very effective, often arriving within 1% of the empirical minimum 100 times earlier than the standard mini-batch algorithm.
In this post, we'll be going through: So far in the series of posts on Machine Learning, we have had a look at the most popular supervised algorithms up to this point. In the previous post, we discussed Decision Trees and Random Forest in great detail. This post and the next few posts will focus on Unsupervised Learning Algorithms, the intuition and mathematics behind them, with a solved Kaggle dataset at the end. Learning tasks done without supervision is unsupervised learning. Unlike supervised machine learning algorithms, there are no labels present in the training data for unsupervised learning which supervise the machine learning model's performance. But, like supervised learning algorithms, unsupervised learning is used for both, discrete and continuous data values.
K-Means, a method of vector quantization that is popular for cluster analysis in data mining, is about choosing the number of clusters, selecting the centroids (not necessarily from the dataset) at random K points, assigning each data point to the closest centroid (forming K clusters), computing and placing the new centroids of each cluster, reassigning each data point to the new closest centroid, and keep repeating the last step until no reassignment takes place. WCSS (Within-Cluster-Sum-of-Squares) is calculated to allow choosing the appropriate number of clusters: the minimal WCSS (decreased to a limit) is chosen as the right number of clusters. Once the number of clusters is chosen, centroids are to be selected, and data points to be assigned to the closet centroids. Afterwards, new centroids are being chosen in the middle of each cluster, and data points are being reassigned to the corresponding cluster. P.S.: k-means is used to prevent choosing wrong initial values, centroids leading to clusters not being the most appropriate.
The k-means is a simple algorithm that divides the data set into k partitions for n objects where k n. In this method, the data set is partitioned into homogeneous groups with similar characteristics. The similarity or dissimilarity is defined by calculating the distance between the centroid and data points. The clusters are formed when the optimization function for the algorithm achieves its objective -- less intracluster distances and more intercluster distances. Repeat steps 3 and 4 until the centroids in clusters change.