# Copy of Machine Learning Algorithms Grouped by Similarity

##### by Orazio A 01/12/2017

## Copy of Machine Learning Algorithms Grouped by Similarity

by Orazio A## 1. Instance-based learning model is a decision problem with instances or examples of training data that are deemed important or required to the model. Such methods typically build up a database of example data and compare new data to the database using a similarity measure in order to find the best match and make a prediction. For this reason, instance-based methods are also called winner-take-all methods and memory-based learning. Focus is put on the representation of the stored instances and similarity measures used between instances.

### 1.1. kNN

### 1.2. Learning Vector Quantization (LVQ)

### 1.3. Self Optimizing Map (SOM)

### 1.4. Locally Weighted Learning (LWL

## 2. Hierarchical Clustering. In data mining and statistics, hierarchical clustering (also called hierarchical cluster analysis or HCA) is a method of cluster analysis which seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two types: Agglomerative: This is a "bottom up" approach: each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy. Divisive: This is a "top down" approach: all observations start in one cluster, and splits are performed recursively as one moves down the hierarchy.

## 3. Clustering, like regression, describes the class of problem and the class of methods. Clustering methods are typically organized by the modeling approaches such as centroid-based and hierarchal. All methods are concerned with using the inherent structures in the data to best organize the data into groups of maximum commonality.

### 3.1. k-means

### 3.2. k-medians

### 3.3. Expectation Maximization

## 4. Like clustering methods, dimensionality reduction seek and exploit the inherent structure in the data, but in this case in an unsupervised manner or order to summarize or describe data using less information. This can be useful to visualize dimensional data or to simplify data which can then be used in a supervised learning method. Many of these methods can be adapted for use in classification and regression.

### 4.1. Principal Component Analysis (PCA)

### 4.2. Partial Least Square Reduction (PLSR)

### 4.3. Sammon Mapping

### 4.4. Multi Dimensional Scaling (MDS)

### 4.5. Projection Pursuit

### 4.6. Principal Component Regression (PCR)

### 4.7. Discriminant Analysis

4.7.1. Linear

4.7.2. Regularized

4.7.3. Quadratic

4.7.4. Flexible

4.7.5. Mixture

4.7.6. Partial Least Squared