With the advent of modern distributed data processing frameworks, organizations do not hesitate one bit before accumulating data. They accumulate data first and then think about how to use it. Data classification in Data mining is the process of looking deep into the accumulated data and deriving insights that can help the business. Typical data mining outcomes include grouping data according to patterns, finding anomalies, deriving relationships, and predictive modeling.
Data Classification and Clustering are two concepts that form the foundation of grouping data. Clustering deals with grouping data without predefined knowledge of the number or type of groups in the data. Classification involves grouping or categorizing data into one of the predefined groups. This article is about Data Classification in Data mining, its different types, and the algorithms used.
Table of Contents
What is Data Mining?
Data Mining is the process of examining huge datasets to find patterns, correlations, and anomalies. Among other things, these datasets contain information from personnel databases, financial information, vendor lists, client databases, network traffic, and customer accounts.
The Data Mining process starts with establishing the business purpose that will be accomplished using the data. Data is then collected from numerous sources and loaded into Data Warehouses, which serve as analytical data repositories. Data is also sanitized, which includes adding missing data and removing duplicates. To detect patterns in data, sophisticated techniques and mathematical models are used.
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Key Features of Data Mining?
Data Mining has the following characteristics:
- Large Datasets and Databases
- Probable Outcome Prediction.
- Pattern Recognition Behavior Analysis is used to make predictions.
- Any SQL phrase can be used to compute a feature from other features.
What is Data Classification?
Data Classification involves selecting a label from a predefined set of labels and assigning them to every data point. Classification has numerous applications in all fields of business, including but not limited to marketing, operations, and finance. Classifying leads according to the probability of conversion is an ever-evolving problem in marketing. Classifying transactions as fraudulent or non-fraudulent is a well-known problem in finance. Classifying products based on predicted profitability to prioritize production is a similar example from the operations field.
Since the labels are predefined, classification can only be done based on knowledge of historical data. Hence classification is a supervised machine learning technique. Rules for classifying unseen data are derived by analyzing past data or building machine learning models based on past data. Building a classifier can be as simple as analyzing the data to manually generate probability values for each label. It can also be as complex as trying out neural network models, tuning their hyperparameters, and arriving at the best model.
Classification is applied to any kind of data depending on the business requirement. It can be applied to numeric, textual, image, or even audio data. While the applied algorithms vary, eventually, all of them boil down to automatically generating the probability distributions in some way or the other. Broadly there are two kinds of classification algorithms. The first kind, named generative algorithms, derives the conditional probabilities of each label by first deriving the joint probabilities.
The second kind, known as discriminatory algorithms, directly arrives at conditional probabilities by focussing on the separation of labels. For the uninitiated, joint probability is the probability of two events happening simultaneously, while conditional probability is the probability of an event occurring, given that another event has already occurred.
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Types of Classification Algorithms
Classification Algorithms can be further divided into statistical and deep learning-based algorithms. Statistical learning generally involves a smaller amount of data, fewer features, and a single instance of a specific algorithm at work. Deep Learning techniques extend statistical learning by creating a deep network of neurons that embed statistical concepts within them.
Naturally, Deep Learning will be able to exploit more complex relationships within data to arrive at labels, But they do take up more resources to run and optimize.
Classification Algorithms are classified into two major types:
Statistical Learning-Based Algorithms
Statistical learning-based algorithms excel when the amount of data is lesser, and the relationships are not complex. They can be trained quickly without high hardware requirements and provide acceptable results. Let us explore a few of them which are commonly used.
1) Naive Bayes Algorithm
This is one of the oldest algorithms and is still considered a one-size-fits-all algorithm for problems with few features and a limited dataset. Naive Bayes is a generative model, which means it calculates the joint probability based on features.
2) Logistic Regression
Logistic Regression predicts the probability of a data row belonging to the label as a zero to one value. A threshold is then defined to assert whether the data element belongs to that class or not. The same concept can be extended for multiple classes to implement a multi-class classifier. Logistic regression does not do a good job in a multi-label classification where each row can belong to more than one label.
3) Support Vector Machines
Classifiers based on support vector machines represent the data points in a multi-dimensional plane and then attempt to derive a hyperplane that divides the data elements according to participation in a target label category. SVM is a discriminatory algorithm. It focuses on separating data points based on features. They are versatile enough to be used in many use cases like natural language processing, image classification, etc.
4) Decision Tree
A Decision tree attempt to divide data based on features starting with a root feature. The root feature is selected based on the degree of effect each feature has in dividing the data. There are multiple methods to select the root feature like Gini impurity, entropy method, etc. Decision trees can work even with very low data volume if a few defining features significantly affect the classification.
5) Random Forest
It is an extension of Decision trees. A random forest is a sequential combination of decision trees created on random data splits. They are preferred over decision trees in case data volume is moderate and many features are significant in dividing the data. Random Forest reduces the probability of the decision tree being stuck with a feature that does not scale well in test data but significantly affects training data. In other words, the randomness in random forests prevents overfitting, which is a common complaint with decision trees.
Deep Learning-Based Algorithms
These algorithms are used when the data volume is high with many defining features. They are capable of extracting more complex relationships compared to statistical learning methods. They are rarely used in exploratory data analysis.
1) Artificial Neural Network
ANNs can be considered a deep network of logistic regressions connected with control gates between them. The network can be expanded in breadth or depth to suit various requirements. It is common to experiment with the breadth and depth of the network to arrive at the best predicting model. The final outcome is usually a probability value in the case of binomial classification. They can be extended to multi-class or multi-label Data classification in Data mining.
2) Convolutional Neural Networks
Convolutions are a technique to reduce the dimensions of the feature set. They are commonly used in the case of images where the base feature set is practically every pixel. Convolutions help to reduce the pixel values to a lesser number of features. They are then connected to a fully connected layer before arriving at the probability.
3) Recurrent Neural Networks
RNNs and their variations like LSTM, GRU, etc., do a good job when the data has a sequential time element. For example, in the case of natural language processing, where the order of words and position is important in Data classification in Data mining, RNN-based classifiers do a good job. They are also used in audio processing, stock data analysis, etc.
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Data Classification in Data Mining
In Data Classification in Data Mining, Algorithms are mainly used as exploratory techniques. Since there are many algorithms available, it is very difficult to provide thumb rules that can help in selecting algorithms.
While there is no perfect algorithm for a specific use case, the below pointers can help during the selection process for Data Classification in Data Mining.
- In Data Classification in Data Mining, It is always better to start with statistical algorithms if the data volume is less. They do well with less data volume and a moderate amount of features.
- In the case of very low data volume and with a few defining features, decision trees will do well compared to SVMs and logistic regressions.
- In the case of complex feature relationships and moderate data volume, SVMs are recommended for Data classification in Data mining.
- In Data Classification in Data Mining, If the data volume and feature set are very high, you may need to explore the deep learning techniques. ANNs are recommended for numeric data.
You have now learned about the basics of Data Classification in Data Mining and the techniques used in implementing Data Classification in Data Mining. Data Classification in Data Mining is an important part of exploring data and, in many cases, helps identify patterns, thereby serving as the foundation for predictive models.
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