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# Top 10 Most Common Data Mining Algorithms You Should Know

Working in Data science every day, I’ve learned about different ways to dig into data. These ways include various data mining techniques and common data mining algorithms. Picture these algorithms as tools that help us find patterns and insights in large amounts of data. Understanding them is crucial for anyone interested in data mining.

In this article, I’ll discuss the top 10 common data mining algorithms. Knowing about these algorithms will give you a better grasp of how data mining works and its applications in real-world scenarios. So, if you’re eager to dive deeper into the world of data science, stick around to learn more!

## 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 data that has already been classified. Classifier here refers to a data mining tool that takes data that we need to classify and tries to predict the class of new data.

Every data point will have its own attributes. The decision tree created by C4.5 poses a question about the value of an attribute and depending on those values, the new data gets classified. The training dataset is labelled with lasses making C4.5 a supervised learning algorithm. Decision trees are always easy to interpret and explain making C4.5 fast and popular compared to other data mining algorithms.

For example, a data set includes information about an individual’s weight, age, and habits (like exercising, eating junk food, etc.). Based on these attributes, you can predict whether the individual is healthy or not. Two categories of classes are “fit” and “unfit.” The C4.5 algorithm obtains a set of already categorized information and then constructs a decision tree that helps in predicting the new items’ class. You may have to use the C4.5 algorithm when working on your final year projects for computer science.

The algorithm learns how to categorize the forthcoming information depending on the preliminary classified data set. C4.5 is a supervised method. In other words, it is a reasonably simple data mining algorithm with human-readable output and clear interpretation.

Every value of attributes creates a new algorithm branch. Every data item receives a proper classification by moving through the branches. This concept of the C4.5 algorithm helps you when working on CSE mini projects.

### 2. K-mean Algorithm

One of the most common clustering algorithms, k-means works by creating a k number of groups from a set of objects based on the similarity between objects. It may not be guaranteed that group members will be exactly similar, but group members will be more similar as compared to non-group members. As per standard implementations, k-means is an unsupervised learning algorithm as it learns the cluster on its own without any external information.

Each item’s metrics are inferred as coordinates in a multi-dimensional space. Every coordinate includes the value of one parameter. The parameter value’s entire set signifies an item vector. For example, you have patient records containing weight, age, pulse rate, blood pressure, cholesterol, etc. K-means can categorize these patients by using the combination of these parameters.

The following section shows the working of the K-means algorithm and it may be useful in your CSE mini projects.

• K-means selects a centroid for each cluster, i.e., a point present in a multi-dimensional space.
• Each patient will be closest located to one of these centroids; they form a cluster around them.
• K-means recalculates each cluster’s center depending on its members. This center works as a new cluster centroid.
• All centroids alter their positions so that patients may be re-classified around each centroid (similar to that in step 2).
• Steps 1-4 will repeat until all centroids remain in place and patients don’t alter their cluster membership. The corresponding state is known as convergence.

### 3. Support Vector Machines

In terms of tasks, Support vector machine (SVM) works similar to C4.5 algorithm except that SVM doesn’t use any decision trees at all. SVM learns the datasets and defines a hyperplane to classify data into two classes. A hyperplane is an equation for a line that looks something like “y = mx + b”. SVM exaggerates to project your data to higher dimensions. Once projected, SVM defined the best hyperplane to separate the data into the two classes.

SVM is a supervised method because it learns on the data set with classes being defined for each item.  One of the most popular examples that outline the Support Vector Machine method is a group of blue and red balls on the table. You can place a pool stick, splitting the blue balls from the red if they are not mixed. In this example, the ball colour is class and the stick works as a linear function that splits the two groups of balls. Furthermore, the SVM algorithm calculates the line’s position that separates them.

The linear function may not work if the balls of different colours are combined in a more complex situation. In that case, the SVM algorithm can project the items into higher dimensions (i.e. hyperplane) to determine the correct classifier.

When considering the plain visual data interpretation, every item (point) contains two parameters (x,y). The classifying hyperplane would have more dimensions if each dot had more coordinates. You can use these concepts of the SVM algorithm when working on your final year projects for computer science.

### 4. Apriori Algorithm

Apriori algorithm works by learning association rules. Association rules are a data mining technique that is used for learning correlations between variables in a database. Once the association rules are learned, it is applied to a database containing a large number of transactions. Apriori algorithm is used for discovering interesting patterns and mutual relationships and hence is treated as an unsupervised learning approach. Thought the algorithm is highly efficient, it consumes a lot of memory, utilizes a lot of disk space and takes a lot of time.

Suppose you have a database consisting of a set of all products sold in a market. Each row in the table corresponds to a customer’s transaction. You can easily check what items every customer purchases. The Apriori algorithm outlines what products are frequently purchased together. Subsequently, it uses this information to enhance the goods’ arrangement to boost sales.

For example, a pair of goods is a set of two items: chips and beer. Apriori calculates these parameters as follows:

Support for each itemset: It denotes the number of times this itemset exists in the database.

Confidence for each item: The conditional probability that indicates what other items customers will buy from the given scope if they buy something.

The entire Apriori algorithm is summarized into 3 steps:

• Join: Calculates the frequency of one item set.
• Prune: The itemsets that fulfill the target support and confidence proceed to the next iteration for two item sets.
• Repeat: The above two steps are iterated for each item set level until you sort the scope’s required size.

You can use these steps of the Apriori algorithm in one of your final year projects for computer science.

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### 5. Expectation-Maximization Algorithm

Expectation-Maximization (EM) is used as a clustering algorithm, just like the k-means algorithm for knowledge discovery. EM algorithm work in iterations to optimize the chances of seeing observed data. Next, it estimates the parameters of the statistical model with unobserved variables, thereby generating some observed data. Expectation-Maximization (EM) algorithm is again unsupervised learning since we are using it without providing any labelled class information.

The EM algorithm is unsupervised since it doesn’t provide labeled class data.  It develops a Math model that predicts how the newly collected data will be distributed depending on the given data set. For example, certain university’s test results show normal distribution. The corresponding division outlines the probability of obtaining each of the probable outcomes.

In this case, the model parameters include variance and mean. The bell curve (normal distribution) defines the whole distribution. Understanding the distribution pattern of this algorithm can help you easily understand your CSE mini projects.

Suppose you have a certain number of exam scores; you only know some portion of them. You don’t have the mean and variance for every data point. But you can estimate the same using the known data samples and determine the likelihood. This implies the probability with which a normal distribution curve with the estimated variance and mean values will accurately describe all the available test results.

EM algorithm helps in data clustering in the following ways:

Step-1: The algorithm attempts to assume model parameters depending on the given data.

Step-2: In the E-step, it calculates each data point’s probability corresponding to the cluster

Step-3: In the M-step, it updates the model parameters.

Step-4: The algorithm iterates Steps 2 and 3 until cluster distribution and model parameters become equal.

These steps of the EM algorithm can be used in some of your mini project topics for CSE 3rd year.

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### 6. PageRank Algorithm

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

It is one of the methods Google uses to determine the relative importance of a webpage and rank it higher on google search engine. The PageRank trademark is proprietary of Google and the PageRank algorithm is patented by Stanford University. PageRank is treated as an unsupervised learning approach as it determines the relative importance just by considering the links and doesn’t require any other inputs.

Several websites link internally, and all of them have their weight in a network. A website attains more votes if more pages are linked to it. Hence, many sources consider it essential and relevant. Every page ranking is formed depending on the linked websites’ class.

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

AdaBoost is a boosting algorithm used to construct a classifier. A classifier is a data mining tool that takes data predicts the class of the data based on inputs. Boosting algorithm is an ensemble learning algorithm which runs multiple learning algorithms and combines them.

Boosting algorithms take a group of weak learners and combine them to make a single strong learner. A weak learner classifies data with less accuracy. The best example of a weak algorithm is the decision stump algorithm which is basically a one-step decision tree. Adaboost is perfect supervised learning as it works in iterations and in each iteration, it trains the weaker learners with the labelled dataset. Adaboost is a simple and pretty straightforward algorithm to implement.

After the user specifies the number of rounds, each successive AdaBoost iteration redefines the weights for each of the best learners. This makes Adaboost a super elegant way to auto-tune a classifier. Adaboost is flexible, versatile and elegant as it can incorporate most learning algorithms and can take on a large variety of data.

### 8. kNN Algorithm

kNN is a lazy learning algorithm used as a classification algorithm. A lazy learner will not do anything much during the training process except for storing the training data. Lazy learners start classifying only when new unlabeled data is given as an input. C4.5, SVN and Adaboost, on the other hand, are eager learners that start to build the classification model during training itself. Since kNN is given a labelled training dataset, it is treated as a supervised learning algorithm.

kNN algorithm doesn’t develop any classifying model. It performs the following two steps when some non-labeled data is inputted.

• It searches for k labeled data points closest to the analyzed one (i.e. k nearest neighbors).
• With the help of the neighbors’ classes, kNN determines what class it must assign to the analyzed data point.

This method needs supervision and it learns from the labeled data set. When you are working on your CSE mini projects, you will find the kNN algorithm straightforward to implement. It can obtain relatively precise results.

### 9. Naive Bayes Algorithm

Naive Bayes is not a single algorithm though it can be seen working efficiently as a single algorithm. Naive Bayes is a bunch of classification algorithms put together. The assumption used by the family of algorithms is that every feature of the data being classified is independent of all other features that are given in the class. Naive Bayes is provided with a labelled training dataset to construct the tables. So it is treated as a supervised learning algorithm.

It uses the assumption that every data parameter in the classified set is independent. It measures the probability that a data point is Class A if it supports features 1 and 2.  It is called the ‘Naive’ algorithm because no data sets exist with all independent features. Essentially, it is merely an assumption that is considered for comparison.

This algorithm is used in many mini project topics for CSE 3rd year because it determines the probability of features based on the class.

### 10. CART Algorithm

CART stands for classification and regression trees. It is a decision tree learning algorithm that gives either regression or classification trees as an output. In CART, the decision tree nodes will have precisely 2 branches. Just like C4.5, CART is also a classifier. The regression or classification tree model is constructed by using labelled training dataset provided by the user. Hence it is treated as a supervised learning technique.

For example, a regression tree output is a continuous or numeric value, like a certain good’s price or the duration of a tourist’s visit to a hotel. You can use the CART algorithm when working on relevant classification or regression problems in the final year projects for computer science.

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## Conclusion

As we wrap up our exploration of the most common data mining algorithms, I can’t emphasize enough how crucial they are for us data science professionals. Understanding these algorithms equips us to uncover valuable insights and make informed decisions with data. Whether it’s predicting future trends or optimizing processes, familiarity with these algorithms is essential.

As we continue to advance in our careers, I believe it’s important for us to apply the knowledge gained from studying these algorithms to drive success and foster innovation in our work.

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

There is no doubt that CART is among the top data mining algorithms used but it does have a few disadvantages. The tree structure gets unstable in case there occurs a minor change in the dataset, thus, causing variance due to unstable structure. If the classes are not balanced, underfit trees get created by the decision tree learners. That is why, balancing the dataset is highly recommended before fitting it with the decision tree.

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

While using the k-mean algorithm for the data mining process, you will have to find a target number which is ‘k’ and it is the number of centroids you need in the dataset. Actually, this algorithm tries to group some unlabeled points into a ‘k’ number of clusters. So, ‘k’ stands for the number of clusters you need by the end.

## In the KNN algorithm, what is meant by underfitting?

As the name suggests, underfitting means when the model doesn’t fit or in other words, is unable to predict the data accurately. Overfitting or underfitting does depend on the value of ‘K’ that you choose. Choosing a small values of ‘K’ in case of a large data set increases the chance of overfitting.