## What is Data Mining Algorithm?

A data mining algorithm is a set of examining and analytical algorithms which help in creating a model for the data. To get a concrete model the algorithm must first analyze the data that you provide which can be finding specific types of patterns or trends. The result of this algorithm is an analysis of different iterations which can help in finding optimal parameters for a proper data mining model. These sets of parameters can be applied across the entire data set and they help in extracting the actionable patterns and getting a detailed statistic of the data.

### Top Data Mining Algorithms

Let us have a look at the top data mining algorithms:

**1. C4.5 Algorithm**

There are constructs that are used by classifiers which are tools in data mining. These systems take inputs from a collection of cases where each case belongs to one of the small numbers of classes and are described by its values for a fixed set of attributes. The output classifier can accurately predict the class to which it belongs. It makes use of decision trees where the first initial tree is acquired by using a divide and conquer algorithm.

Suppose S is a class and the tree is leaf labeled with the most frequent class in S. Choosing a test based on single attribute with two or more outcomes than making this test as root one branch for each outcome of the test can be used. The partitions correspond to subsets S1, S2, etc. which are outcomes for each case. C4.5 allows for multiple outcomes. In the case of complex decision trees, C4.5 has introduced an alternative formula, which consists of a list of rules, where these rules are grouped together for each class. To classify the case the first class whose conditions are satisfied is named as the first one. If no rule is satisfied by the case, then it is assigned a default class. The C4.5 rulesets are formed from the initial decision tree. C4.5 enhances the scalability by multi-threading.

**2. The k-means algorithm**

This algorithm is a simple method of partitioning a given data set into the user-specified number of clusters. This algorithm works on d-dimensional vectors, D={xi | i= 1, … N} where i is the data point. To get these initial data seeds, the data has to be sampled at random. This sets the solution of clustering a small subset of data, the global mean of data k times. This algorithm can be paired with another algorithm to describe non-convex clusters. It creates k groups from the given set of objects. It explores the entire data set with its cluster analysis. It is simple and faster than other algorithms when it is used with other algorithms. This algorithm is mostly classified as semi-supervised. Along with specifying the number of clusters it also keeps learning without any information. It observes the cluster and learns.

**3. Naïve Bayes Algorithm**

This algorithm is based on Bayes theorem. This algorithm is mainly used when the dimensionality of inputs is high. This classifier can easily calculate the next possible output. New raw data can be added during the runtime and it provides a better probabilistic classifier. Each class has a known set of vectors which aim at creating a rule which allows the objects to be assigned to classes in the future. The vectors of variables describe the future objects. This is one of the easiest algorithms as it is easy to construct and does not have any complicated parameter estimation schemas. It can be easily applied to huge data sets as well. It does not need any complicated iterative parameter estimation schemes and hence users who are unskilled can understand why the classifications are made.

4.7 (3,220 ratings)

View Course

**4. Support Vector ****Machines Algorithm**

If a user wants robust and accurate methods, then Support Vector machines algorithm must be tried. SVMs are mainly used for learning classification, regression or ranking function. It is formed on the basis of structural risk minimization and statistical learning theory. The decision boundaries must be identified which is known as a hyperplane. It helps in the optimal separation of classes. The main job of SVM is to identify the maximizing the margin between two classes. The margin is defined as the amount of space between two classes. A hyperplane function is like an equation for the line, y= MX + b. SVM can be extended to perform numerical calculations as well. SVM makes use of kernel so that it operates well in higher dimensions. This is a supervised algorithm and the data set is used to first let SVM know about all the classes. Once this is done then SVM can be capable of classifying this new data.

**5. The Apriori Algorithm**

To find the frequent itemsets from a transaction data set and derive association rules the Apriori algorithm is widely used. To find frequent item sets is not difficult because of its combinatorial explosion. Once we get the frequent itemsets then it is clear to generate association rules for larger or equal specified minimum confidence. Apriori is an algorithm which helps in finding frequent data sets by making use of candidate generation. It assumes that the item set or the items present are sorted in a lexicographic order. After the introduction of Apriori data mining research has been specifically boosted. It is simple and easy to implement. The basic approach of this algorithm is as below:

**Join**: The whole database is used for the hoe frequent 1 item sets.**Prune**: This item set must satisfy the support and confidence to move to the next round for the 2 item sets.**Repeat**: Until the pre-defined size is not reached till then this is repeated for each itemset level.

### Conclusion

With the five algorithms being used prominently, there are others as well which help in mining data and also learn. It integrates different techniques including machine learning, statistics, pattern recognition, artificial intelligence and database systems. All these help in analyzing large sets of data and perform different data analysis tasks. Hence they are the most useful and reliable analytics algorithms.

### Recommended Articles

This has been a guide to Data Mining Algorithms. Here we discussed the basic concepts and top data mining algorithms. You can also go through our other suggested articles to learn more-