Data Mining Algorithms in Python

What is Data Mining?

Data Mining is a process of extraction of knowledge and insights from the data using different techniques and algorithms. It can use structured, semi-structured, or unstructured data stored in different databases, data lakes, and warehouses.

The main purpose of data mining is to search patterns that can predict the data and make decisions from it. The process of data mining consists of multiple steps: data exploration with the help of various techniques like clustering, classification, association rule mining, clustering, etc. Data mining is lined with multiple studies or disciplines like machine learning, statistics, and artificial intelligence which help extract the data. The insights gathered after data extraction can be used in various industries like research, fraud detection, etc.

Need for Data Mining

Data mining has the ability to determine the patterns and relationships from a huge amount of data from different sources. There are different tools that are used for data mining that convert the data into useful insights. It can detect patterns and insights from unrelated bits of data. Raw data is not useful for any industry, as studying raw data can give inaccurate results. It may have irregularities, missing data, anomalies, etc.; thus, it needs to be cleaned before it is mined.

Working of Data Mining

Data Ming includes various steps: determining the problem, data collection, data cleaning, data exploration, data modeling, implementation, and then evaluation of the results.

1. Firstly, we will determine the problem statement. It involves determining the goals and objectives of the problem.
2. We need to collect the data from multiple sources and then identify the data needed for the problem.
3. The next step is to clean the data, which is called Data cleaning. It checks for any null, incorrect, or duplicate values and cleans them from the data to make it more understandable and easy to analyze. Then, it modifies the data, transforms it into a useful format, and checks errors.
4. The next step is data exploration, which includes visualization and statistics to get insights and know its characteristics.
5. The next step is to create a model which can be used to predict or forecast the data. It includes model fitting, checking its accuracy, etc.
6. Then, it will validate the model's performance to check the accuracy. This is done by using the validation set.
7. Now, it can be implemented to make predictions and insights from the data using an environment. This step includes the deployment of the model and integrating it.
8. The last step is to evaluate the results of the model and its efficiency.

Data Mining Techniques and Algorithms

There are several techniques used for data mining. This includes:

Classification

A data mining function is used to initialize samples in a dataset to target classes. The classifiers are used to implement the classification algorithms for data mining. It includes two steps: training and classification. Training is a process of feeding the data to a specified class and creating a classifier according to the data. Classification is a process of feeding the trained data to the classifier and then giving unknown data to the classifier to predict the class of the sample input.

Python provides the sklearn library, in which there are different classification algorithms.

Different Classification algorithms are K-NN, decision tree, naïve Bayes, etc.

Clustering

The process of grouping the data into clusters based on similar features (generally, the nearest neighbors of the margin) is called clustering. Clustering is used to implement with unlabelled data. In this, we have to analyze the data by grouping it into clusters. This technique of converting the data into clusters is also called unsupervised data analysis. The cluster-based data mining has various algorithms. The most common and widely used algorithm in clustering is the k-means algorithm.

We can implement the clustering algorithms using the sklearn library in Python.

Different clustering algorithms are k-means clustering, DBSCAN, etc.

Regression

The data mining technique used to predict the numerical values in the data set is called regression. It tells the relation between the dependent variables and independent variables. It is also called the supervised data mining technique. The regression is based on the equation of a straight line. Fitting the curve or the straight line to a set of data points is called the regression.

The regression algorithms can be implemented with the sklearn library in Python.

There are different Regression algorithms, including, Linear Regression, Multiple Regression, Logistic Regression, Lasso Regression, etc.

Association

The association is a data mining technique that is used to represent the relationship between different variables that may be unnoticeable. It is used to analyze and predict customer behavior. It is used in market analysis, product clustering, catalog design, etc.

Now, let's understand the different algorithms used for data mining.

• K-means Clustering Algorithm

K-means is a type of clustering data mining algorithm that divides the data into multiple groups or clusters based on characteristics and similarity of data. It takes the parameter k (number of clusters) from the users and groups the similar data into the same cluster such that the similarity outside the cluster differs from the data inside the cluster. The mean value of the cluster can determine the similarity.

• Support Vector Machine

Support Vector Machine is a supervised algorithm for data mining. It can be used for both regression and classification problems. However, it is best suited for the classification technique of data mining. It uses a hyperplane to classify the data into two classes. The hyperplane divides the data points such that the margin between the closest point of both classes has the maximum distance. It mostly works on a 2D plane with 2 features.

• AdaBoost

AdaBoost is also a data mining algorithm based on the classification technique. It is based on both classification and regression techniques. It is a kind of supervised data mining technique to classify the weak learning models to the strong learner. It gets some data and then predicts a new set of data.

• PCA

Principal Components Analysis is a type of unsupervised data mining technique used to analyze the relationship between different sets of variables. The main purpose of PCA is to reduce the dimensionality of the data set. It searches for a new set of variables from the original set of variables, which reduces the dimensions of the data. It is used for both classification and regression of the data.

• Collaborative Filtering

Collaborative filtering is a data mining technique mostly used in recommendation systems to find similar users and recommendations. It is based on the classification technique of data mining. It classifies the users rather than using the features for recommendation.

• Apriori Algorithm

The apriori algorithm is an association-based data mining algorithm used in databases to identify the items in the data set and generate association based rules on the dataset. It helps to determine relationships and patterns in the data set by frequently searching for the items occurring together.