Ten data mining algorithms

1. C4.5

C4.5 algorithm is a classification decision tree algorithm in machine learning algorithm, and its core algorithm is ID3 algorithm. The C4.5 algorithm inherits the advantages of the ID3 algorithm, and improves the ID3 algorithm in the following ways:

1) Using the information gain rate to select the attribute, overcomes the disadvantage of choosing the attribute with the information gain to choose the value;

2) pruning in the process of tree construction;

3) be able to complete the discretization of the continuous properties of the processing;

4) Ability to process incomplete data.

The C4.5 algorithm has the following advantages: The resulting classification rules are easy to understand and the accuracy rate is high. The disadvantage is that in the process of constructing the tree, the data sets need to be scanned and sorted several times, which results in the inefficiency of the algorithm.

2. The K-means algorithm is the K-means algorithm

The K-means algorithm algorithm is a clustering algorithm that divides n objects into K-partitions, K < n, according to their attributes. It is similar to the maximum expected algorithm for dealing with mixed normal distributions, as they all try to find the center of natural clustering in the data. It assumes that the object attributes come from the space vector, and that the goal is to minimize the sum of the mean squared errors within each group.

3. Support Vector Machines

Support Vector machines, the English-supported vector machine, referred to as SV Machine (generally referred to as SVM in the paper). It is a kind of supervised learning method, which is widely used in statistical classification and regression analysis. Support Vector machines map vectors to a higher dimensional space, where a maximum interval of hyperspace is established in this space. On both sides of the super plane that separates the data, there are two super-planes that are parallel to each other. The separation of the superelevation plane maximizes the distance of two parallel super-planes. It is assumed that the larger the distance or gap between parallel planes, the smaller the total error of the classifier. An excellent guide is c.j.c Burges's "Pattern Recognition Support vector machine Guide". Van der Walt and Barnard compare support vector machines with other classifiers.

4. The Apriori algorithm

Apriori algorithm is one of the most influential algorithms for mining Boolean association rule frequent itemsets. The core is the recursive algorithm based on the two-stage frequency set theory. The association rule belongs to single-dimension, single-Layer and Boolean association rules in classification. In this case, all itemsets with support degrees greater than the minimum support are called frequent itemsets, or frequency sets.

5. Maximum expectation (EM) algorithm

In statistical computation, the maximal expectation (em,expectation–maximization) algorithm is the algorithm for finding the maximum likelihood estimation of parameters in the probability (probabilistic) model, in which the probabilistic model relies on the invisible hidden variables (latent VARIABL). The greatest expectations are often used in the field of data aggregation (clustering) for machine learning and computer vision.

6. PageRank

PageRank is an important part of Google's algorithm. The U.S. patent was granted in September 2001, and the patent owner is one of Google's founders, Larry Page. As a result, the page in PageRank is not a webpage, it refers to Paige, that is, the hierarchical method is named after page.

PageRank measures the value of the website based on the number and quality of the external links and internal links of the site. The concept behind PageRank is that each link to a page is a poll of that page, and the more links it has, the more votes are being voted on by other sites. This is the so-called-link popularity ‖--measure how many people are willing to hook up their site to your site. The concept of PageRank is quoted as a quote from an academic paper-the more times people are quoted, the more authoritative it is to judge the paper.

7. AdaBoost

AdaBoost is an iterative algorithm whose core idea is to train different classifiers (weak classifiers) for the same training set, and then set up these weak classifiers to form a stronger final classifier (strong classifier). The algorithm itself is achieved by changing the distribution of data, which determines the weights of each sample based on the correctness of the classification of each sample in each training set and the accuracy of the last population classification. The new data set that modifies the weights is sent to the lower classifier for training, and finally the classifier that is trained at the end of each training is fused as the final decision classifier.

8. Knn:k-nearest Neighbor Classification

K Nearest neighbor (k-nearest NEIGHBOR,KNN) classification algorithm is a theoretically mature method and one of the simplest machine learning algorithms. The idea of this approach is that if a sample is in the K most similar in the feature space (that is, the nearest neighbor in the feature space) Most of the samples belong to a category, then the sample belongs to that category.

9. Naive Bayes

In many classification models, the two most widely used classification models are decision tree (decision tree model) and naive Bayesian model (Naive Bayesian MODEL,NBC). Naive Bayesian model originates from classical mathematics theory, has a solid mathematical foundation, and stable classification efficiency. At the same time, the NBC model has few parameters to estimate, less sensitive to missing data, and simpler algorithm. In theory, the NBC model has the smallest error rate compared to other classification methods. But this is not always the case, because the NBC model assumes that the properties are independent of each other, and this hypothesis is often not true in practice, which has a certain effect on the correct classification of the NBC model. When the number of attributes is more or the correlation between attributes is large, the efficiency of the NBC model is inferior to the decision tree model. The performance of the NBC model is best when the attribute correlation is small.

CART: Classification and regression tree

CART, classification and Regression Trees. There are two key ideas under the classification tree. The first is the idea of recursively dividing an argument space, and the second idea is to prune it with validation data.

Ten data mining algorithms