Data mining is a technique that allows us to obtain patterns or models from the collected data. It aims to extract meaningful information from huge chunks of datasets using data mining techniques and data mining algorithms and use them in decision-making. In this blog, we have listed some of the most popular data mining algorithms used by data scientists and data miners.
What Are Data Mining Algorithms?
Data mining algorithms are computational techniques and methods used to extract meaningful and valuable patterns, insights, and knowledge from massive datasets. Data mining algorithms are designed to automatically discover hidden patterns, trends, associations, and correlations within data that may not be readily apparent to humans. Listed below are the most popular types of data mining algorithms.
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As the name suggests, a decision tree is a sequence of decisions organized hierarchically, exactly like the branches of a tree. Those algorithms accept both numerical and categorical data. The decision trees algorithm is frequently applied for classification, grouping, and forecasting tasks. If they predict categories, they are often called classification trees. They are called regression trees if they are numerical and intended to be predicted.
Data miners use C4.5 algorithms to generate a decision tree using data samples. It is an extension of the previous Quinlan ID3 Algorithm. The decision trees generated by C4.5 are used in data classification, and thus, C4.5 is often referred to as a statistical classifier. C4.5 algorithm is described as “a landmark decision tree program that is probably the most widely used machine learning workhorse in practice to date”.
The Apriori Algorithm is an iterative approach mainly used in the frequent mining of data sets until the most frequent set of items is achieved. It involves two steps, namely ‘join’ and ‘prune’ to reduce search space. It is an iterative approach to discovering the most frequent itemsets. The algorithm is a sequence of steps to dig in and find the most frequent set of elements in the given database.
Artificial Neural Networks
Artificial neural networks are very powerful algorithms, and they contribute to problem-solving. These algorithms involve steps like classification, prediction, and grouping. Artificial neural networks are organized into layers, where the first one is the input layer, then the hidden layer, and finally the output layer.
One of the disadvantages of artificial neural networks is that they work with numerical data. Categorical variables are usually discretized to apply these algorithms.
The PageRank algorithm is a base algorithm for search engines. Scores and estimates the relevance of a particular piece of data within a large set, such as a single website within a larger set of all Internet websites
Expectation-Maximization (EM) is a clustering algorithm that defines parameters by analyzing the data and predicts the possibility of a future exit or random event within the data parameters. EM is used as a clustering algorithm, just like the k-means algorithm for knowledge discovery. EM algorithm works in iterations to optimize the chances of seeing observed data. It also forecasts the parameters of the statistical model with unobserved variables and generates observed data.
The AdaBoost or Adaptive Boosting algorithm works within other learning algorithms that anticipate behaviour based on observed data to be sensitive to statistical extremes. It is a statistical classification meta-algorithm that can modify the EM algorithm's output by analysing the extreme's relevance.
If you need to solve dimensionality problems with categorical variables, you can use correspondence analysis to carry out this task. Two verticals are used in correspondence analysis –
Simple correspondence analysis evaluates two variables; It is based on the contingency table.
Multiple correspondence analysis, which considers more than two variables, refers to Burt’s table.
Multidimensional scaling is used to graphically represent through a perceptual map the similarities that you have objects in a data cloud, considering the positioning between them. Multidimensional scaling looks a lot like cluster analysis; the only difference is that in this model, the variables to determine similarity are unknown, while in the cluster, they are.
K-Means Clustering Algorithms
K-media clustering is one of the simplest and most popular unsupervised machine learning algorithms. Typically, unsupervised algorithms make inferences from data sets using only input vectors without referring to known or labelled results.
K-Nearest Neighbors (KNN) Algorithms
KNN algorithm recognizes patterns in the location of the data and associates them with the data with a larger identifier. For example, if you want to map a post office to each home's geographic location and have a data set for each home's location, the KNN algorithm will map homes to the closest post office based on their proximity.
Naive Bayes Algorithms
The Naive Bayes Algorithm is a probabilistic machine learning algorithm based on Bayes’ Theorem, used in a wide variety of classification tasks. It predicts the output of an identity based on data from known observations. For example, if a person is 6 feet 6 inches (1.97 m) tall and wears a size 14 shoe, the Naive Bayes algorithm could predict with a certain probability that the person is a male.
“CART” is an acronym for Classification and Regressive Tree Analysis. Like decision tree analysis, it organizes data according to competing options, such as whether a person has survived an earthquake. Unlike decision tree algorithms, which can only classify one output or one numerical output based on regression, the CART algorithm can use both to predict the probability of an event.
The CART algorithm is structured as a sequence of questions, the answers to which determine what, if any, the next question will be. The result of these questions is a tree-like structure where the ends are terminal nodes, at which point there are no more questions.
Data Mining has been widely used across different domains including retail, business planning, marketing, banking, and cyber security, among others, and has become an essential tool for data-driven businesses. You can add value to your data science projects and meet real-world business goals by accurately using data mining algorithms. Hope this article helped you understand what type of data mining algorithms are used in data mining tasks. You can also read some of our related articles to understand data mining in detail –
- Data Mining in E-commerce: Frequent Itemset Mining, Association Rules, and Apriori Algorithm Explained
- Data Mining Architecture in Data Mining Systems
- Data Preprocessing in Data Mining – The Basics
- Knowledge Discovery in Databases (KDD) in Data Mining
- Decision Trees in Data Mining
FAQs - Data mining algorithms
How do data mining algorithms work?
Data mining algorithms analyze data, search for patterns or relationships, and use mathematical and statistical techniques to extract valuable information.
What is the difference between supervised and unsupervised data mining algorithms?
Supervised algorithms are used for classification or prediction tasks with labelled data, while unsupervised algorithms uncover patterns and relationships without predefined labels.
Can you provide an example of a real-world application of data mining algorithms?
Predictive maintenance in manufacturing uses data mining algorithms to anticipate equipment failures and schedule maintenance before breakdowns occur.
What are the challenges in implementing data mining algorithms?
Challenges in implementing data mining algorithms include:
- Data quality issues.
- Selecting the suitable algorithm.
- Handling large datasets.
- Ensuring privacy and ethical considerations.
Are there open-source data mining algorithms available?
Yes, many open-source libraries like scikit-learn, Weka, and TensorFlow offer various data mining algorithms for various tasks.