# Data Mining Algorithms – 13 Algorithms Used in Data Mining

## 1. Objective

In our last tutorial, we studied **Data Mining Techniques**. Today, we will learn Data Mining Algorithms. We will try to cover all types of Algorithms in Data Mining: Statistical Procedure Based Approach, Machine Learning Based Approach, Neural Network, Classification Algorithms in Data Mining, ID3 Algorithm, C4.5 Algorithm, K Nearest Neighbors Algorithm, Naïve Bayes Algorithm, SVM Algorithm, ANN Algorithm, 48 Decision Trees, Support Vector Machines, and SenseClusters.

So, let’s start Data Mining Algorithms.

## 2. What is Data Mining Algorithms?

- A bank loan officer wants to analyze the data in order to know which customer is risky or which are safe.

- A marketing manager at a company needs to analyze a customer with a given profile, who will buy a new computer.

**Do you know Which Tools used in Data Mining?**

## 3. Why Algorithms Used In Data Mining?

Here, are some reason which gives the answer of usage of Data Mining Algorithms:

- In today’s world of “big data”, a large database is becoming a norm. Just imagine there present a database with many terabytes.

- As facebook alone crunches 600 terabytes of new data every single day. Also, the primary challenge of big data is how to make sense of it.

- Moreover, the sheer volume is not the only problem. Also, big data need to diverse, unstructure and fast changing. Consider audio and video data, social media posts, 3D data or geospatial data. This kind of data is not easily categorized or organized.

- Further, to meet this challenge, a range of automatic methods for extracting information.

## 4. Types of Algorithms In Data Mining

Here, 13 Data Mining Algorithms are discussed-

### a. Statistical Procedure Based Approach

### b Machine Learning Based Approach

### c. Neural Network

**Follow this link to know more about Neural Network**

### d. Classification Algorithms in Data Mining

### e. ID3 Algorithm

- Every element in the subset belongs to the same class (+ or -), then the node is turned into a leaf and

- labeled with the class of the examples

- If there are no more attributes to select but the examples still do not belong to the same class. Then the node is turned into a leaf and labeled with the most common class of the examples in that subset.

- If there are no examples in the subset, then this happens. Whenever parent set found to be matching a specific value of the selected attribute.

- For example, if there was no example matching with marks >=100. Then a leaf is created and is labeled with the most common class of the examples in the parent set.

**Working steps of Data Mining Algorithms is as follows,**

- Calculate the entropy for each attribute using the data set S.

- Split the set S into subsets using the attribute for which entropy is minimum.

- Construct a decision tree node containing that attribute in a dataset.

- Recurse on each member of subsets using remaining attributes.

### f. C4.5 Algorithm

### g. K Nearest Neighbors Algorithm

### h. Naïve Bayes Algorithm

### i. SVM Algorithm

**SVM**

### J. ANN Algorithm

**Artificial Neural Network**, follow this link

**Follow this link to Know about Advantages of Data Mining**

### K. 48 Decision Trees

### l. Support Vector Machines

### M. SenseClusters (an adaptation of the K-means clustering algorithm)

## 5. Conclusion

As a result, we have studied Data Mining Algorithms. Also, we have learned each type of Data Mining Algorithms. Furthermore, if you feel any query, feel free to ask in a comment section.

See Also –**Data Mining and Knowledge Discovery**

Of the following algorithms

A. C4.5 decision tree

B. One-R

C. PRISM covering

D. 1-Nearest Neighbor

E. Naive Bayes

F. Linear Regression

1.1. Which one(s) are fast in training but slow in classification?

1.2. Which one(s) produce classification rules?

1.3. Which one(s) require discretization of continuous attributes before application?