Article published by : Infocampus training institute on Sunday, December 24, 2017 - Viewed 145 times

Category : Software

Top 10 Data Mining Algorithms, Explained



A data mining definition
Once you know what they are, how they work, what they do and where you can find them, my hope is you’ll have this blog post as a springboard to learn even more about data mining.
What are we waiting for? Let’s get started!
Here are the algorithms:
• 1. C4.5
• 2. k-means
• 3. Support vector machines
• 4. Apriori
• 5. EM
• 6. PageRank
• 7. AdaBoost
• 8. kNN
• 9. Naive Bayes
• 10. CART
We also provide interesting resources at the end.
1. C4.5
What does it do? C4.5 constructs a classifier in the form of a decision tree. In order to do this, C4.5 is given a set of data representing things that are already classified.
Wait, what’s a classifier? A classifier is a tool in data mining that takes a bunch of data representing things we want to classify and attempts to predict which class the new data belongs to.
What’s an example of this? Sure, suppose a dataset contains a bunch of patients. We know various things about each patient like age, pulse, blood pressure, VO2max, family history, etc. These are called attributes.
2. k-means
What does it do? k-means creates k groups from a set of objects so that the members of a group are more similar. It’s a popular cluster analysis technique for exploring a dataset.
Hang on, what’s cluster analysis? Cluster analysis is a family of algorithms designed to form groups such that the group members are more similar versus non-group members. Clusters and groups are synonymous in the world of cluster analysis.
Is there an example of this? Definitely, suppose we have a dataset of patients. In cluster analysis, these would be called observations. We know various things about each patient like age, pulse, blood pressure, VO2max, cholesterol, etc. This is a vector representing the patient.
3. Support vector machines
What does it do? Support vector machine (SVM) learns a hyperplane to classify data into 2 classes. At a high-level, SVM performs a similar task like C4.5 except SVM doesn’t use decision trees at all.
Whoa, a hyper-what? A hyperplane is a function like the equation for a line, y = mx + b. In fact, for a simple classification task with just 2 features, the hyperplane can be a line.
4. Apriori
What does it do? The Apriori algorithm learns association rules and is applied to a database containing a large number of transactions.
What are association rules? Association rule learning is a data mining technique for learning correlations and relations among variables in a database.
What’s an example of Apriori? Let’s say we have a database full of supermarket transactions. You can think of a database as a giant spreadsheet where each row is a customer transaction and every column represents a different grocery item.
5. EM
What does it do? In data mining, expectation-maximization (EM) is generally used as a clustering algorithm (like k-means) for knowledge discovery.
In statistics, the EM algorithm iterates and optimizes the likelihood of seeing observed data while estimating the parameters of a statistical model with unobserved variables.
6. PageRank
What does it do? PageRank is a link analysis algorithm designed to determine the relative importance of some object linked within a network of objects.
Yikes.. what’s link analysis? It’s a type of network analysis looking to explore the associations (a.k.a. links) among objects.
Here’s an example: The most prevalent example of PageRank is Google’s search engine. Although their search engine doesn’t solely rely on PageRank, it’s one of the measures Google uses to determine a web page’s importance.
7. AdaBoost
What does it do? AdaBoost is a boosting algorithm which constructs a classifier.
As you probably remember, a classifier takes a bunch of data and attempts to predict or classify which class a new data element belongs to.
But what’s boosting? Boosting is an ensemble learning algorithm which takes multiple learning algorithms (e.g. decision trees) and combines them. The goal is to take an ensemble or group of weak learners and combine them to create a single strong learner.
What’s the difference between a strong and weak learner? A weak learner classifies with accuracy barely above chance. A popular example of a weak learner is the decision stump which is a one-level decision tree.
8. kNN
What does it do? kNN, or k-Nearest Neighbors, is a classification algorithm. However, it differs from the classifiers previously described because it’s a lazy learner.
What’s a lazy learner? A lazy learner doesn’t do much during the training process other than store the training data. Only when new unlabeled data is input does this type of learner look to classify.
9. Naive Bayes
What does it do? Naive Bayes is not a single algorithm, but a family of classification algorithms that share one common assumption:
Every feature of the data being classified is independent of all other features given the class.
What does independent mean? 2 features are independent when the value of one feature has no effect on the value of another feature.
10. CART
What does it do? CART stands for classification and regression trees. It is a decision tree learning technique that outputs either classification or regression trees. Like C4.5, CART is a classifier.
Is a classification tree like a decision tree? A classification tree is a type of decision tree. The output of a classification tree is a class.
For example, given a patient dataset, you might attempt to predict whether the patient will get cancer. The class would either be “will get cancer” or “won’t get cancer.”
What’s a regression tree? Unlike a classification tree which predicts a class, regression trees predict a numeric or continuous value e.g. a patient’s length of stay or the price of a smartphone.

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