Algorithms in data mining: Top 10 algorithms define

Algorithms in data mining are sets of inferences and calculations that create models from data. To create a model, an algorithm first analyzes the data provided and looks for certain types of patterns or trends.

We work in data science every day and learn about different ways to mine data. These methods include various data mining techniques and common data mining algorithms. Imagine these algorithms as tools that help you find patterns and insights in large amounts of data. Understanding these is very important for anyone interested in data mining.

This article describes the top 10 popular data mining algorithms. Learning about these algorithms will help you better understand how data mining works and its application in real-world scenarios.

Top 10 data mining algorithms

1. C4.5 Algorithm

C4.5 is one of the top data mining algorithms and was developed by Ross Quinlan. C4.5 is used to generate a classifier in the form of a decision tree from a set of previously classified data. Here, a classifier refers to a data mining tool that takes data that needs to be classified and tries to predict classes of new data.

Each data point has its own characteristics. The decision trees created by C4.5 raise questions about the values of attributes, and new data is classified according to those values. The training dataset is labeled with laces, making C4.5 a supervised learning algorithm. Decision trees are always easy to interpret, which is why C4.5 is fast and popular compared to other data mining algorithms.

For example, the dataset includes information about a person’s weight, age, and habits (exercise, junk food consumption, etc.). On the basis of these characteristics, we can estimate whether a person is healthy or not. There are two categories of classes: “conforming” and “non-conforming.” The C4.5 algorithm takes a set of already classified information and builds a decision tree that helps in predicting the class of a new object. helps. If you are working on a final-year project in computer science, you may need to use C4.5 algorithms.

The algorithm learns how to classify future information according to a pre-classified dataset. C4.5 is a supervised method. In other words, it is a fairly simple data mining algorithm with human-readable output and a clear explanation.

A new algorithm branch is created for each value of the attribute. All data items are appropriately classified while passing through the branches. This concept of C4.5 algorithms will be useful while working on CSE mini-projects.

2. K-mean Algorithm

One of the most common clustering algorithms, K-means, works by creating k groups from a set of objects based on the similarity between them. Although there is no guarantee that group members are completely similar, group members are more similar than non-group members. Following the standard implementation, K-Means is an unsupervised learning algorithm that learns clusters on its own without external information.

The metrics for each item are estimated as coordinates in multidimensional space. Each coordinate contains the value of a parameter. The complete set of parameter values represents an item vector. For example, suppose you have a patient record that includes weight, age, pulse rate, blood pressure, cholesterol, etc. K-means can use a combination of these parameters to classify these patients.

The next section shows how the K-Means algorithm works. This can be useful for small CSE projects.

• K-means selects the centroid of each cluster, a point located in multidimensional space.
• Each patient is located closest to one of these centers of gravity. They form groups around themselves.
• K-means recalculates the center of each cluster based on its members. This center will serve as the new cluster centroid.
• All centroid locations are changed, so the patient is reclassified around each centroid (same as step 2).
• Steps 1-4 are repeated until all centroids are in place and the patient does not change cluster membership. The corresponding state is known as convergence.

3. Support Vector Machines

In terms of function, support vector machines (SVMs) work very similarly to C4.5 algorithms, except that they do not use decision trees at all. SVM learns a dataset and defines a hyperplane that classifies the data into two classes. A hyperplane is the equation of a straight line like y = mx + b. SVM exaggerates the projection of data into higher dimensions. Once interpolated, the SVM defines an optimal hyperplane to separate the data into two classes.

SVM is a supervised method because it learns a dataset with a defined class for each item. One of the most common examples to illustrate support vector machine technology is a set of blue and red balls on a table. Place the billiard stick and separate the blue and red balls if they are not mixed. In this example, the color of the ball is a square, and the stick acts as a linear function that separates the two groups of balls. Additionally, the SVM algorithm calculates the location of the line separating them.

Linear functions may not work if balls of different colors are combined in more complex situations. In that case, the SVM algorithm can project the items to a higher dimension (i.e., a hyperplane) to determine the correct classifier.

Considering simple visual data interpretation, each item (point) has two parameters (x, y). The more coordinates each point has, the greater the dimensions of the classification hyperplane. You can use these concepts of SVM algorithms while working on your computer science final year project.

4. Apriori Algorithm

A primary algorithm works by learning association rules. The association rule is a data mining technique used to learn the correlation between variables in a database. Once the correlation rule is learned, it is applied to databases with large numbers of transactions. The Apriori algorithm is regarded as an unsupervised learning approach because it is used to discover interesting patterns and interactions. This algorithm appears to be very efficient, but it consumes a lot of memory, uses a lot of disk space, and takes a lot of time.

Suppose we have a database that contains the set of all products sold in the market. Each row of the table corresponds to a customer transaction. Easily see which products each customer is purchasing. The Apriori algorithm identifies which products are most often purchased together. Then use this information to improve product placement and increase sales.

For example, a paired product is a set of two products: potato chips and beer. Apriori calculates these parameters as follows:

Support for each item set: It shows how many times this item is present in the database.

Confidence for each item: If a customer buys something, the conditional probability of which other items from a specified range he will buy.

The entire Apriori algorithm can be summarized in three steps.

• Join: Calculates the frequency of a set of objects.
• Prune: Itemsets that meet target support and reliability advance to the next iteration of the two itemsets.
• Repeat: The above two steps are repeated for each item level until the desired size of the scope is sorted.

5. Expectation-Maximization Algorithm

Expectation maximization (EM) is used as a clustering algorithm, similar to the K-means algorithm for knowledge discovery. EM algorithms work iteratively to optimize the opportunity to confirm observed data. The unobserved variables are then used to estimate the parameters of the statistical model that generated the observed data. The Expectation Maximization (EM) algorithm is still unsupervised learning because it is used without providing labeled class information.

The EM algorithm does not provide labeled class data and is therefore not supervised. Develop a mathematical model that predicts how newly collected data will be distributed based on a given dataset. For example, examination results from a university exhibit a normal distribution. The corresponding partition outlines the probability of each possible outcome.

In this case, the model parameters include the variance and the mean. The bell curve (normal distribution) defines the entire distribution. Understanding the distribution pattern of this algorithm will help you understand the CSE Mini Project easily.

Let’s say you have a certain number of test scores. You only know some of them. I don’t know the mean and variance of all the data points. However, you can estimate it by using known data samples to determine the probability. This refers to the probability that a normal distribution curve with an estimated variance and mean accurately describes all available test results.

The EM algorithm helps in data clustering in the following ways:

• Step 1: The algorithm attempts to hypothesize model parameters based on the data provided.
• Step 2: The E step calculates the probability of each data point corresponding to a cluster.
• Step 3: The M step updates the model parameters.
• Step 4: The algorithm repeats steps 2 and 3 until the cluster distribution and model parameters are equal.

These steps of the EM algorithm can be used as part of CSE 3rd year mini-project topics.

6. PageRank Algorithm

PageRank is commonly used by search engines like Google. It is a link analysis algorithm that determines the relative importance of linked objects within a network of objects. Link analysis is a type of network analysis that examines the relationships between objects. Google Search uses this algorithm by understanding the backlinks between web pages.

This is one of the methods Google uses to determine the relative importance of a web page and rank it higher in the Google search engine. The PageRank trademark is the proprietary property of Google, and the PageRank algorithm is patented by Stanford University. PageRank is considered an unsupervised learning approach because it determines relative importance by only considering links and does not require any other inputs.

Many websites have internal links, and they play an important role in all networks. The more pages linked to a website, the more votes that website will receive. Therefore, many sources consider it necessary and relevant. The ranking of each page is determined according to the category of the linked websites.

Google assigns page rank from 0 to 10. This ranking is based on the relevancy of the page and the number of outbound, inbound, and internal links. You can use this unsupervised algorithm while working on web-related mini-project topics for CSE 3rd year students.

7. Adaboost Algorithm

AdaBoost is a boosting algorithm used to build classifiers. A classifier is a data mining tool that takes data and predicts the class of the data based on the input. A boosting algorithm is a collective learning algorithm that executes and combines multiple learning algorithms.

Boosting algorithms take a group of weak learners and combine them to create a stronger learner. Weak learners classify data with less accuracy. The best example of a weak algorithm is the decision stamp algorithm, which is essentially a one-step decision tree. Adaboost works in iterations, training a weak learner using a labeled dataset in each iteration, making it perfect for supervised learning. Adaboost is a very simple algorithm that is easy to implement.

After the user specifies the number of rounds, the weights for each best learner are redefined in each subsequent AdaBoost iteration. This makes AdaBoost a very elegant way to auto-tune a classifier. Adaboost is flexible, versatile, and elegant, as it can cover most learning algorithms and handle a wide variety of data.

8. kNN Algorithm

KNN is a lazy learning algorithm used as a classification algorithm. Lazy learners do nothing except save the training data during the training process. Lazy learners only start classifying when they are given new, unlabeled data as input. On the other hand, C4.5, SVN, and Adaboost are eager learners that start building classification models right during training. Since KNN is given a labeled training dataset, it is considered a supervised learning algorithm.

The KNN algorithm does not develop any classification models. If unlabeled data has been entered, perform the following two steps:

• Find the k closest labeled data points (or k nearest neighbors) to the analyzed data point.
• KNN decides which class should be assigned to the analyzed data point with the help of neighboring classes.

This method requires supervision and is learned from a labeled dataset. As you work on the CSE mini-project, you will notice that the KNN algorithm is easy to implement. It gives relatively accurate results.

9. Naive Bayes Algorithm

Although Naive Bayes is not a single algorithm, we can see that it works efficiently as a single algorithm. Naive Bayes is a collection of classification algorithms. The assumption used in this set of algorithms is that all features of the data being classified are independent of all other features given within the class. Provides a labeled training dataset for building Naive Bayes tables. Therefore, it is considered a supervised learning algorithm.

It uses the assumption that all data parameters in the classified set are independent. It measures the probability that a data point is in class A if it supports feature 1 and feature 2. This is called a “naive” algorithm because no data set containing all independent features exists. Basically, it is just an assumption kept in mind for comparative purposes.

This algorithm is used in many CSE 3rd year mini-project subjects as it determines the probability of a feature based on class.

10. CART Algorithm

CART stands for Classification and Regression Tree. It is a decision tree learning algorithm that provides a regression or classification tree as output. In CART, a decision tree node has exactly two branches. Like C4.5, CART is also a classifier. A regression or classification tree model is built using a labeled training dataset provided by the user. Therefore, it is considered as a supervised learning method.

For example, the output of a regression tree is a continuous or numerical value, such as the price of a particular product or the length of stay of a tourist at a hotel. You can use CART algorithm while working on related classification or regression problems in your computer science final year project.

Conclusion

As we finish exploring the most common data mining algorithms, we cannot emphasize enough how important they are for us data science professionals. Understanding these algorithms can help you gain valuable insights and make informed decisions based on your data. Whether you want to predict future trends or optimize processes, becoming familiar with these algorithms is essential.

As we move forward in our careers, we believe it is important to apply the knowledge gained from research on these algorithms to achieve success at work and foster innovation.

If you want to learn more about data science, I highly recommend checking out IIIT-B. We also highly recommend checking out Grad’s Executive PG Program in Data Science, which is designed to help working professionals upskill without leaving their jobs. The course offers 1:1 with industry mentors, easy EMI options, IIIT-B alumni status, and more. Learn more about.

Frequently Asked Questions (FAQs)

1. What are the limitations of using the CART algorithm for data mining?

Although CART is undoubtedly one of the most commonly used data mining algorithms, it has some shortcomings. Small changes in the dataset will cause the tree structure to become unstable, resulting in dispersion due to the unstable structure. If the classes are imbalanced, the decision tree learner creates an underfit tree. Therefore, we strongly recommend that you balance your dataset before fitting it into a decision tree.

2. What exactly does ‘K’ mean in the k-means algorithm?

If we use the K-Means algorithm for the data mining process, we need to find the target number ‘k’. This is the number of centroids you want in your dataset. In fact, this algorithm tries to group some unlabeled points into ‘k’ groups. Therefore, ‘k’ denotes the number of clusters ultimately required.

3. In the KNN algorithm, what is meant by underfitting?

As the name suggests, underfitting means that the model does not fit or, in other words, cannot accurately predict the data. Overfitting or underfitting depends on the value of ‘K’ chosen. Choosing a smaller value of ‘K’ increases the chances of overfitting when the dataset is large.

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