Data mining is the process of finding patterns and repetitions in large datasets and is a field of computer science. Data mining techniques and algorithms are being extensively used in Artificial Intelligence and Data Science. There are many algorithms but let’s discuss the top 10 in the data mining algorithms list.
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.
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.
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.
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.
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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
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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.
7. Adaboost Algorithm
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.
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.
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
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So here are the top 10 data from the data mining algorithms list. We hope this article has shed some light on the basis of these algorithms.
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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.