ML and AI Algorithms for Big Data Classification
As you know Big Data involves working with huge chunks of both structured and unstructured data. The volume of data that data scientists have to work on sometimes exceeds over millions of rows and it becomes too tedious to prepare for the work, albeit doing it. That is when these technologies become interdisciplinary. With machine learning and artificial intelligence, a data scientist can make his or her work of process Big Data easily. Considering the volume of data sets, software models and conventional databases turn out to be less effective. This is exactly when machine learning can be applied to Big Data.
Machine Learning Algorithms
Like we mentioned in one of our previous blog articles, machine learning is an integral part of Artificial Intelligence. There are three types of algorithms in machine learning that can be used for Big Data classification – Supervised, semi-supervised and unsupervised.
As far as supervised learning algorithms, some of the most commonly used ones include –
- Maximum Entropy Method (MaxENT)
- Support Vector Machines (SVM)
- Naïve Bayes
- Boosting Algorithm and more
Classification and regression are two classifications of supervised learning. Classification is when the class attribute of a set is discrete and regression is when it is continuous. Without getting too technical, let us simply understand that some of the classification methods include
- Decision Tree Learning
- k-Nearest Neighbour
- Naïve Bayes Classifier
When it comes to regression techniques, they include linear and logistic regression techniques.
In unsupervised learning, the algorithms take unlabelled data and classify it by drawing a comparison among data features. That is why you can find algorithms in use such as –
- Clustering – which can be again classified into hierarchal, density-based, K-means
- Adaptive Resonance Theory
- And Self-organizing Maps
Clustering can be further classified into three categories (this can take a little while for comprehension) – supervised clustering, unsupervised clustering and semi-supervised clustering.
Supervised clustering works on identifying clusters with high-probability densities with respect to individual classes! Supervised clustering works best when there are target variables and training sets that include the variables to cluster.
When a measure of dissimilarity or similarity is presented, unsupervised clustering reduces the intercluster similarity and increases intracluster similarity. It works on a very specific object function and that is why hierarchal and k-Means are two of the most popular clustering techniques in unsupervised learning.
Apart from the similarity parameter, this class of clustering makes use of adjusting or guiding domain information in order to improve clustering. This guiding or adjusting domain information could be pairwise constraints prevalent between the target or observation variables for some observations.
Apart from these, there are algorithms such as support vector machines, which are binary classifiers; decision trees, which are used to classify data depending on its feature value and more.
For a beginner, these are some of the first-level of insights you need to know about the algorithms used in Big Data classification. Like we always mention, practical exposure always helps you understand complexities like these. So, if you haven’t started off with an artificial intelligence or a machine learning course, it is high time you did.
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