Data mining algorithms are computational algorithms used to create models from data by extracting patterns from that data, finding the best parameters for a mining model, and then using that model in data-driven methods.
Below, we’ll learn what data mining is, why it’s useful, and we’ll look at a few examples of data mining algorithms.
Data Mining in a Data Pipeline Nutshell
Data mining is process of extracting data from a data set and turning it into a model.
Know the factors affecting employee performance
It is one stage in the data science pipeline, a multi-stage process that aims to use information for data science-related fields such as machine learning and artificial intelligence.
The stages in this pipeline include:
In this pipeline, data mining would fall between exploration and modeling. Also known as knowledge discovery in data, data mining is designed to “mine” patterns and information, which can then be transformed into models, stories, and actionable business insights.
Types of Data Mining Algorithms
Here are a few examples of different types of data mining algorithms:
- Regression. Regression algorithms are useful for prediction. They attempt to establish a relationship between a dependent and independent variable and then make forecasts based on that relationship.
- Classification. Classification algorithms categorize data into a number of classes, which are then assigned labels. It does this by examining the dataset as it is received and then classifying new inputs based on these classifications.
- Segmentation. Segmentation algorithms separate data into regions. These are useful for unlabeled data that don’t have categories. These algorithms are closely related to clustering algorithms, and some use the two terms interchangeably.
- Association. Association algorithms attempt to discover how items are related to one another. It does this by looking for rules that govern the relationships between variables in databases.
- Sequences. Sequences analysis imposes order on observations that must be preserved when training models. In many of the other algorithms, the sequence is not important, but with sequence analysis, the order is important.
Experienced data miners will use more than one algorithm to achieve the model that is most useful for their data strategy.
10 Examples of Data Mining Algorithms
Let’s look at a few examples of algorithms used in data mining:
C 4.5 is a type of decision tree algorithm.
This algorithm goes through a series of decisions to classify existing data and predict upcoming data.
As data moves through the branches of this decision tree, it is assigned to a classification.
Expectation-Maximization is a clustering algorithm.
Clustering algorithms, as the name suggests, find patterns based on how data is “clustered” according to certain attributes – that is, it segregates data based upon individual groups. When looking at a graph, we can easily see data points that are clustered closely together.
This algorithm consists essentially of two steps. First it attempts to calculate the probability of a data point belonging to a cluster, then it updates the model parameters. These steps are repeated continuously until closer distribution equalizes.
3. Naïve Bayes Algorithm
These algorithms are based on Bayes’ theorems.
This is a lightweight algorithm that can be used to generate mining models quickly, which can then be further refined with additional algorithms for more accurate results.
4. Linear Regression Algorithm
A linear regression algorithm is similar to a decision tree.
It calculates the linear relationship between a dependent and independent variable.
That relationship, which looks like a line when plotted out on a graph, is useful for prediction.
Variations on the linear regression algorithm include nonlinear regression algorithms.
5. Sequence Clustering
Sequence clustering is a combination of sequence analysis and clustering.
First the algorithm finds sequences, then it looks for clustering to find similar sequences.
By itself, clustering algorithms focus on clusters of cases with similar attributes.
The Expectation-Maximum algorithm and the sequence clustering algorithm above are examples of types of clustering algorithms.
CART stands for classification and regression trees.
Using this approach, you can generate either ranging or decision trees as an output. Is it similar but more advanced than C4.5.
8. Time Series
Time series algorithms provide multiple algorithms that can be useful for predicting continuous values.
These are useful algorithms end of day can make predictions based on a single data set. Other algorithms, however, often require additional information to be input.
9. K-Means Clustering
This is a type of clustering algorithm, or a set of techniques, that views items as points in space.
The K-means algorithm defines a center point for each cluster and gradually regroups clusters closer to those centers. After the algorithm is complete, the clusters, which may have consisted of a single large set of noisy data, are now separated into groups.
This is an unsupervised algorithm that looks for association rules in big data sets. Unsurprisingly, this is an example of an association algorithm, mentioned above.
It performs three essential tasks: joining items, pruning that set, and repeating these steps until complete.