The Best Data Mining Algorithms
You may be familiar with data mining. An attractive and, of course, a specialized field beneficial for many businesses which its job opportunities are increasing globally. Data mining uses various algorithms to analyze the data. In this article, we intend to introduce the best data mining algorithms.
What is data mining?
Before introducing the best data mining algorithms, we provide a simple definition of data mining. Data mining is the knowledge that analyzes the data entered into a system. Based on new algorithms and tools, this knowledge provides useful statistics for business owners and helps them stay ahead of their competitors by planning their activities.
Today, this science’s value is so well known that in large companies, before deciding and planning to conduct specialized campaigns or designing expensive products, seek to obtain public data to define their path by analyzing them.
The best data mining algorithms
Classification and clustering are methods used to analyze data. The following algorithms are used in these methods:
K-means: one of the best data mining algorithms.
One of the most popular and best data mining and machine learning algorithms is the K-means. In this method, we first randomly select the desired number of K points from the available points and consider them as the center of the clusters (Centroid). In fact, k is also the number of clusters. Then we get the distance of each point to the centroid. The points near each centroid belong to that cluster, and so the type of clustering and the position of each point change.
In the next steps, the middle of the points is considered the center of the cluster, and this process is repeated until the position of the points is fixed, and the clusters do not change. Each collection in data mining is a set of points with the most similar features in the input DataSet. K-means is used for data clustering and is one of the primary data mining algorithms.
C4.5 algorithm, a developed model of the ID3, is known as one method to create a decision tree. Utilizing this algorithm, you can use data to make a decision tree and use the decision tree as an index for classification. Each node in this tree has attributes measured by the gain of information criterion and is selected as an index of class separation.
Support Vector Machines algorithm
Support Vector Machines algorithm has various applications in the field of machine learning. This data mining algorithm analyzes the data used for classification and regression methods. In each data space, a set of points are responsible for demarcating and categorizing data. Support Vector Machines algorithm classifies points using its criterion, which is Support Vectors.
This algorithm is used to describe the classification of data.
Naive Bayes algorithm
The Naive Bayes algorithm is one of the data mining algorithms based on probabilistic classification techniques. The algorithm uses the Bayes’ Theorem in mathematics and determines the probability of occurrence by determining independent variables and categorizes the data, base on this. This is just one of the many Bayes family algorithms used in data analysis. The Naive Bayes algorithm has many applications in text classification and retrieval and can predict user behavior for businesses.
A Priori algorithm
Apriori is a popular data mining algorithm that can find related data and determine the degree of dependency in each category. This classical algorithm uses association rules to receive input items (for example, customer transactions) and then categorize them. The performance of this algorithm continues until there are no similar items in different categories.
PageRank (PR) algorithm
This algorithm, as its name implies, is used to rank web pages. Google search engines use this algorithm to identify the importance of web pages and organize them for users. Therefore, another application of this algorithm is in the field of SEO. PageRank (PR) uses the statistics of inbound links as well as their quality to review and compare websites.
Neural network, of the best data mining and machine learning algorithms.
One of the best data mining algorithms in solving complex difficulties is the Neural Network algorithm, which in addition to data mining, is also much discussed in areas such as machine learning and deep learning. This algorithm also finds similarities between data and tags, classifies them, and offers different data analysis models. In addition to business, the neural network algorithm is also considered in predicting stock market rates and economic issues.
The K-Nearest Neighbors algorithm compares each new data with the previous data and places it in the category where the new and old data are similar. In fact, it falls into a class that is more similar to the surrounding data, in other words, to its immediate neighbors. This algorithm is non-parametric and does not base its analytical assumptions on the previous data distribution model. This algorithm is one of the data classification methods.