Data mining constitutes the backbone of data science. Raw data may be an extremely valuable commodity, but without being processed and refined, it will remain like a diamond in the rough. The use of data classification techniques allows us to properly categorize data and use it to its full potential.
What is classification?
In data mining, classification is an organizational technique used to separate data points into a variety of categories. The data classification process is commonly performed with the help of AI-powered machine learning tools.
Modern classification techniques hold a close relationship with machine learning. Elements and variables in a data set can be reorganized by AI in predetermined groups or classes. Sophisticated data classification AI solutions can use statistics, linear and logistic regression, decision trees, neural nets, and many other techniques to aid in the data mining process.
Purpose of data classification
Analysis of customers, employees, and marketplace feedback data can provide valuable insights on the performance of any enterprise and even help in future decision-making. The goal of classification is to portray data in its highest possible quality, thereby improving the overall efficiency of the data mining process.
Importance of data classification
Data classification systems have become a regular aspect of our everyday lives. The AI behind the spam folder in your inbox, for instance, uses data classification techniques to filter emails. The use of classification has become crucial to maintaining a clean and efficient data environment.
How does data classification work?
The data classification work process begins with a learning step, where training data is fed to an algorithm. Afterward, a classification rule or a set thereof is developed, which will be used by the algorithm to analyze the test data and produce new results.
It doesn’t matter what data classification method you use, it will most likely consist of the use of a classification algorithm. Since data classification relies heavily on pattern recognition, an AI-powered machine learning solution can be given the proper instructions of what to look out for and the AI will handle all the heavy lifting.
Popular types of classification algorithms
Logistic Regression
The logic behind logistic regression is quite simple, as it works exclusively with binary outcomes. Under the eye of this algorithm, the data may be affirmative or negative, true or false, passed or failed, and any other outcome with only two plausible answers.
Working with a binary framework means that the algorithm can work with any kind of true or false statement. This means that a logistic regression algorithm can be used for purposes such as identifying an object in a picture by comparing it with its own set of gathered examples, a feature commonly used by image recognition software. It can also be used to filter offensive language online.
Naive Bayes
This algorithm calculates the probability of a particular piece of data belonging to a category, and then classifies it accordingly. It can be used to analyze vast amounts of data to find particular snippets that relate to a certain subject.
K-Nearest Neighbors
In a similar manner to logistic regression, this algorithm works with associations to classify data, the difference being that the K-Nearest Neighbors algorithm associates a piece of data with another based on how proximate, or similar, they are.
Decision Trees
Data classified by this algorithm is placed under categories and then further sub-categorized based on more distinguishable values. It is easier to understand decision trees when thought of as flow charts, where the most general values are placed on the top and then elements are sub-selected based on more detailed information.
Support Vector Machines
This algorithm works by creating a separation between pieces of data it understands as different. This separation is known as a hyperplane and it can be thought of as a division line, separating two categories.
Types of data classification
The different types of data classification include:
- User-based classification
- Content-based categorization
- Context-based categorization
- Category hierarchy
- Category drill-down
- Sentiment override
User-based classification
No data classification rule states that the process must be done strictly by software. User-based classification is the most simple and bare-boned classification method. It depends on someone manually categorizing the data, without the help of any software solution. The end-user is in charge of making their own selections and categories while relying on their knowledge and discretion in the handling of information.
Content-based categorization
Data classification can take a content-based approach through the inspection and interpretation of sensitive information within a file. Classification techniques such as fingerprinting, regular expression, or Bayesian engines are common solutions when it is required for AI to look for information inside of a particular piece of data.
Context-based categorization
This classification method consists of an algorithm searching for key elements related to the files under analysis. Sensitive information may present itself in different variables, and the algorithm will discern the nature of the data using contextual information gathered from its environment.
Information variables used by this classification model include:
- The way the data is being used
- The user(s) or users who normally interact with the data
- The location where the data is being accessed from or transferred to
- The time at which the data is commonly interacted with
Category hierarchy
A category hierarchy is a classification method that uses a level-based structure to categorize data, akin to a family tree. Every classification level on the hierarchy can be host to distinct categories, sub-categories, and classifiers. Users of the Semeon platform can use a dynamic drag-and-drop feature to easily reorganize their hierarchies or create new ones.
Category drill-down
Users can drill down classification categories to find specific information relevant to their search. By exploring certain sentences attached to a particular piece of data, for instance, a user can know who imparted a particular comment and the context behind it.
Sentiment override
Semeon provides its users with AI-powered solutions able to learn and adapt to become more efficient. Artificial intelligence can determine how users prefer the evaluation of their data and act accordingly. Furthermore, it can improve visualization options to better cater to the needs of the end-user.
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Benefits of data classification
From classification projects being used to predict the behavior of diseases to increasing guest satisfaction in the hospitality industry, properly categorized data can be extremely beneficial to all areas of a business in any industry.
A practical case study performed on the improvement of guest experiences in the hospitality industry proved that access to properly categorized data can substantially help improve customer sentiment. The analysis consisted of more than 7000 reviews, comments, and survey responses to show the structural strengths and weaknesses of a prestigious hotel in New York City.
The study allowed the hotel to understand what their customers value most about their guest experience, and could therefore adapt its strategies to maximize customer satisfaction. Insights were also useful to shape future marketing efforts, as a more accurate idea of what guests would take away from their hotel experience was devised.
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Applications of classification algorithms
Technology surrounds us and this happened so rapidly that we never truly had the time to understand its inner workings appropriately. Here are demonstrative applications of classification algorithms:
- Keeping spam away from your inbox
- Sentiment analysis
- Single and multi-omics data analysis
- Image classification
Keeping spam away from your inbox
Monitoring for spam and dealing with it is one of the most popular purposes of a classification algorithm. Not only does spam classification keep our inboxes clean, but it also helps us identify potential phishing scams or other malicious content that may be present in our inbox.
A common classification method used by email applications consists of having an algorithm analyze the text contents of an email. The algorithm will look out for suspicious content like scamming keywords or faulty spelling of a recipient’s name, and will then make the decision of placing the email on the spam folder or leaving it be.
Sentiment analysis
Sentiment analysis is somewhat similar to conducting a poll, with the difference of it being automated and conducted by machine learning technology. Data classification systems can be used to analyze the written opinion of thousands of online users and catalog their experiences as positive, negative, or neutral.
For instance, a new product release prompts thousands of people to go online and express their feelings about their user experience. By performing a sentiment analysis, we could have factual data about the overall online audience’s opinion of the product.
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Single and multi-omics data analysis
Single and multi-omics data refers to branches of science that collectively receive the moniker “Omics” due to all having the suffix -omics tied to their name. Multi-omics data allows scientists to contrast huge data sets, thereby producing better opportunities to analyze the complex systems living beings are made of.
The term multi-omics alludes to the existence of single-omics data, which happens to be a branch of multi-omics itself. Single-omics data analysis, or single-cell multi-omics, provides an analysis of a single cell on a multilevel transition.
Image classification
Algorithms can be trained to understand the contents of an image and produce an accurate account of what is being portrayed. The more an algorithm is trained for this purpose, the better it becomes. In fact, it can become too good for modern standards.
On November 2nd, 2021, Facebook’s VP of Artificial Intelligence published an update where it was declared that the app would stop using its image recognition algorithm and delete the facial data of over a billion people (source). The reason cited by spokespeople is the growing societal concern about the unparalleled accuracy of the software, especially from legislators who aren’t sure what is the best way to regulate it.
