What You Need to Know About Data Mining Algorithms
A data mining algorithm is a set of calculations used to create models from data. The algorithm analyzes the data first by looking for specific patterns or trends for it to create the model.
The results of the analysis are then used by the algorithm repetitively, and this leads to the best parameters which are used to create the mining model. The parameters are then used in the entire data set to extract detailed statistics and actionable patterns.
Common data mining algorithms are provided by SQL Server and are the used for deriving models from data. Data mining algorithms from Microsoft are fully programmable and can be customized using the provided APIs.
Data mining components can also be used in integration services. The most common uses are to automate the creation, training and retaining of data models. The following are the commonly used data mining algorithms.
C4.5 constructs classifiers in the form of a decision tree. For C4.5 to be able to do this, it is fed with a set of data which represents already classified items. Classifiers take a bunch of data representing things, which need to be categorized and then tries to predict which class the data belongs to.
The ease of interpretation and explanation are the best selling points for decision trees. These points are very fast, common and the output can be read by almost everyone very easily. C4.5 utilizes these points.
Thus you can be assured of the best results when used. C4.5 is used by some of the most open source data visualization and analysis tools in their decision tree classifiers.
K-means is a common algorithm used for analyzing clusters with the aim of exploring a particular dataset. This algorithm creates K groups from a set of objects to make members of a group look more similar.
K-means can be classified as either supervised or unsupervised. However, many people classify it as unsupervised. Apart from specifying the number of clusters, k-means is usually able to determine the clusters on its own without relying on any information about which cluster an observation belongs to.
Simplicity is what makes k-means to be preferred by many users. Its simplicity makes it faster and efficient compared to other algorithms, especially over larger data sets. K-means can also be used to pre-cluster large datasets, and conduct cluster analysis on the sub-clusters.
Sensitivity to the initial choice of centroids and outliers are the two main weaknesses of K-means. But what you should understand is that the algorithm was developed to operate on continuous data. This means that it can be more challenging for it to work on discrete data.
Support vector machines (SVM)
Support vector machines use hyperplane to classify data into two classes. SVM can perform similar tasks like C4.5 in some circumstances, but it doesn’t use decision trees under any circumstance.
Hyperplane can be referred to a function like the equation for a line. The hyperplane can be a line in case the simple classification task has only two features. SVM can project your data into higher dimensions. It then determines the best hyperplane required to separate the data in the two classes.
Support vector machine is a supervised algorithm since it relies on the dataset to determine its classes. Only after that, the SVM can be able to classify new data.
SVM and C4.5 are the commonly used classifiers. Interpretability and kernel selection are some of the SVM weaknesses.
SVM can be implemented in numerous places, but the common implementations are libsvm, scikit-learn and MATLAB.
Expectation-maximization is generally used in data mining as a clustering algorithm for knowledge discovery. In statistics, EM repeats and optimizes the possibilities of finding observed data, while predicting the parameters of a statistical model with unobserved variables.
EM is an unsupervised learning since it doesn’t provide labeled class information. The algorithm is very simple and can be implemented easily. This makes it be adopted by many users. EM doesn’t have any weaknesses.