Many different aspects of data mining and machine learning seem incredibly complicated. They use variables and mathematical models that appear opaque at first. However, all of these tools have a simple series of goals. They want to make sense of data through either sorting or predictions. Once a newcomer understands those goals, many of the machine learning algorithms in data science make more sense. An example of one of these is the k-nearest neighbors algorithm. K-nearest neighbors may not mean much to the outside observer. To some, it may seem hopelessly complicated. However, k-nearest neighbors is actually a clear, simple way to bring together data and to sort it into categories that make sense.
The k-nearest neighbors algorithm is a machine learning algorithm based on the variable k. K is chosen by a number of different means. It is often a small integer and is sometimes higher k values increase error rates while lower k values create less-cohesive categories. This variable ends up being the number of closest neighbors that a data point is evaluated against. The algorithm looks at each data point and compares it with k number of neighbors. It uses the distance between the point and the neighbors to categorize the data point in one way or another.
This approach is not quantified and is solely focused on relationships between points of data and different classes. There are no means to work with like in k-means clustering. Instead, the algorithm uses those distances to draw distinct categories and then fill those categories while avoiding errors. In the end, there is a clear, concise list of data-filled categories or a clean regression line from the relationships between different data points and k. That product can later be used to train a machine into reproducing the set from other groups of data.
K-nearest neighbor is a common algorithm with a wide variety of applications. can be utilized through many forms of machine learning. One of these is supervised learning. Supervised learning uses the tools of an algorithm to achieve a result based upon an example set. In k-nearest neighbors, a supervised learning tool would create an example set of categories already filled with the data points being considered. There is also the possibility that the example set might be a line of regression.
Either way, the supervised learning design would then run with the k-nearest neighbors algorithm producing either categories or a line or regression. The machine learning algorithm would then have an output that it could compare to the original. This system could respond by tweaking different weights or variables and then running the test again and again. Over time, the machine learning algorithm would be able to reproduce the example set within a particular margin of error. Finally, the machine learning algorithm could then sort future sets using the same tools and weights that it used to originally achieve the example set. This function allows a categorization and regression tool to later be used in predictive scenarios.
There is also unsupervised learning which happens outside of the purview of the example set. In unsupervised learning, k-nearest neighbors would change categories and regression lines based only on a broad set of guidelines and perhaps an established k value. This form of learning would allow the algorithm and the machine to determine more categories and relationships between data points in those categories. While there would not be the organization of supervised learning, the relationships revealed may be more surprising and productive for the operator of the artificial intelligence system.
Different architectures help to support supervised and unsupervised learning with k-nearest neighbors. One of these is an artificial neural network. The artificial neural network can be crafted of many different nodes. Nodes, in this case, may be neighbors or possible k variables. They may also be particular categories that the algorithm is placing data items into.
The artificial neural network works by weighting those nodes individually in order to allow the algorithm to do its work. After the first created output, the weights of the nodes can then be changed depending on the desired output. The artificial neural network can be used in this way to accommodate both supervised and unsupervised learning.
Classification and regression are the most important uses of k-nearest neighbors. K-nearest neighbors can create numerous categories based on the distance between neighbors and then place information into those categories. It can process thousands of data points and place them into a large number of categories based on a few parameters.
Classification helps make sense of a host of numbers that may have no other connection to one another. A complete set of categories can reveal relationships that were not otherwise apparent. It can also be helpful in predictive modeling. The same is true for regression. K-nearest neighbors can be used to perform regression and note the trajectory of a particular set of data points. This information can be used to project how the data will develop over time.
K-nearest neighbors is a simple, qualitative way to organize and understand a data set. It also helps to facilitate machine learning just as well, if not better, than many other algorithms currently in use today. K-nearest neighbors will not solve every problem inherent to the understanding of data. However, anyone who hopes to fully understand data mining needs to become adept at using this powerful tool in their data mining efforts.