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These two terms are used in Machine Learning for Data Mining and Data Science models. As we all know, within Machine Learning, there are Supervised and Unsupervised Learning methods.

Data Classification/Categorization=Supervised Learning

Data Clustering=Unsupervised Learnings

Both these terms are used for patterns identification before complex algorithms come into action.

Classification is done based on the uniqueness of the data where e.g., all animals are tagged separately whereas Clustering is done based on the characteristics e.g., 1) Animals with 2 legs 2) Animals with 4 legs.

• Data Classification uses labeled data whereas Data Clustering uses unlabeled data.
• The most popular Data Classification algorithms in data mining are the K-Nearest Neighbor and decision tree algorithms.
• The two common Data Clustering algorithms in data mining are K-means clustering and hierarchical clustering.
• Data Classification output is known.
• Data Clustering out is unknown.
• Training data is provided for Data Classification.
• No training data is provided for Data Clustering.

This is a very confusing topic as both Categorization and Classification are used interchangeably.

Both Categorization and Classification are Metadata and are used for Data Governance as well.

Categorization is based on the attributes and properties of entities in dataset whereas Classification is based on the business requirements.

As you can refer to the image, data has been categorized in passport, health record, financial record, job posting etc., and at the same time these categories are classified as Medium Risk.

‘Both these terms fall under Supervised Learning in Data Science domain.’

For example, there are grandparents, parent, children, cousins, servants, gardener, machinic, car painter etc., all these falls under categories but there can be further classifications i.e., family members and non-family members.

Categories: Grandparents, Parent, Children, Cousins, Gardener, Machinic, Car painters

‘Data Segmentation is about dividing dataset into multiple sections based on some requirements.’

Segments are defined based on many parameters e.g., in a business, segments are defined by accessibility, actionable, profitable etc.

For example, when we go into a mall, there is a segment/section for Food Court where multiple categories of food are available with different classification like expensive or cheap food.

Steps to improve Data Segmentation

• Enrich your data to increase data quality.
• Plan and replan your data segmentation criteria(s)
• Improve real time segmentation.
• Design segment based on customer prioritization and feedback.
• Integrate with as many data elements.

In business, what you can do by having Data Segmentation

• You can create deals based on customer buying behavior.
• You can improve your products arrangement.
• You can plan your marketing strategy.
• You can analyze customer feedback.

Data Labelling is about tagging something which is not identifiable coming from source. The Labelling can be on known data and on unknown data as well.

Let’s try to understand the relationship among all these 3 terms i.e., Data Labelling, Data Classification and Data Clustering.

• Data Clustering group the data based on existing parameter and falls under Unsupervised Learning.
• Data Labelling is the process of tagging data based on some defined parameter.
• Data Classification group the data based on manually LABELLED data and falls under Supervised Learning.

Since the era of unstructured dataset, data is coming in many formats. There are specialized tools which can tag or label the unstructured data set so it can become input for Machine Learning (ML) Models. There are also certain libraries used in ML Models to label unstructured data set which can then become input of another ML Model for Supervised Learning.

Data Annotation and Data Labelling works together to prepare dataset for Machine Learning Models in Data Science practice.

‘Data Annotation is the technique of doing Data Labelling.’

Data Annotation is a complete process with many steps to curate, whereas Data Labelling is ONE of the many steps in it.

Data Labelling is about tagging the data for training e.g., machine learning models whereas Data Annotation is the technique for tagging the data so ML models can reuse it as well. Data Labelling tags are used by ML models for future pattern recognitions whereas Data Annotation is about recognizing the data via a process.

Just like Data Labelling, Data Annotation is also for supervised machine learning to prepare data as an input and to store it for future reference. Not to mention, there are STILL many organizations doing manual Data Labelling. Data Annotation is about the process so Data Labelling can be automated for accuracy, eliminating human intervention. Bad Data Labelling can impact the outcome of applications.

Entropy term is normally used in Data Science domain.

• Where finding unexpected outcomes is the aim
• Where surprises are welcomed
• Where results are analyzed based on probability ratio, the lower the probability the better it is.
• Where informative information is something which wasn’t known

Types

• High Entropy means, more surprises, more unexpected values are considered more informative.
• Low Entropy means, less surprises, less unexpected values are considered less informative.

‘Entropy is also called as Extreme Disorder of values.’

Referring to the image, we can see at the starting point all the signs are MINUS, then in the middle there are 50/50 signs of PLUS & MINUS and right at the end all the signs are PLUS. At those extremes left, middle and right corners, the Entropy is at the lowest, so no surprises are expected so nothing much for Data Scientists to predict right. But in 2^nd, 4^th, and 5^th circles, it’s difficult to say how many PLUSES(s) and MINUS(s), if values are not visible, at this points Entropy is at the highest.

Epistemology, a term rooted in the Greek word episteme, meaning “knowledge,” and logos, meaning “discourse” or “communication,” explores the concept of “justified knowledge.” It addresses questions like “What do we know?”, “How can we claim to know something?”, “What validates our beliefs?”, and “How do we ascertain the certainty of our knowledge?”

In the realm of data, these questions take on a new dimension, especially in the Big Data era, where we store vast amounts of raw data, structured, semi-structured, and unstructured. Data science teams apply machine learning or deep learning models to extract insights, turning raw data into knowledge. However, to ensure that these insights align with their origins, a “justification of knowledge” is essential, ensuring that what we extract truly reflects what was expected. This process emphasizes evidence-based validation, where qualitative research relies on the quality of design and data collected.

Data Hierarchy is a known term and most of us are aware of it but sometimes, it’s confused with Data Taxonomy & Data Ontology.

Data Taxonomy and Data Ontology are metadata for the purpose of Data Governance whereas Data Hierarchies presents actual data. A very well-known and famous example is the Organizational Chart where hierarchy starts from CEO and goes down to Database Administrator in data domain.

• ‘Data Taxonomies apply to identified objects or phenomena.’’
• ‘Data Ontologies apply to terms, concepts, or categories.’
• ‘Data Hierarchy is a chain of relationship in actually data.’

Family tree is another simple example.

Data can be grouped in a way where data exploration and representation become easy. For example, at database level, hierarchy can be Database 🡪 Files/ Tables 🡪 Records 🡪 Fields/ Columns 🡪 Bytes 🡪 Bits.

Same line Data Taxonomy, Data Hierarchy also have single parent and multiple child relationship among the rows of dataset.

Data Anonymization, Data Pseudonymization or Data De-identification all worked the same way and related to Data Privacy.

Data Anonymization is about removing or modifying personal information before sharing with others so those people whom the data about remains anonymized.

‘Data Anonymization is about hiding the identity of a person to be undetectable.’

For example, while implementing a data solution it is required to share data with development team, so before sharing one must make sure data is anonymized, persons details are not exposed to development team members e.g., name, contact number, address, credit card number etc.

Data Anonymization is normally enforced by Regulatory Bodies and not following can result in heavy penalties and a bad reputation for any organization.

Techniques [All these terms are covered separately]

• Data Masking
• Data Pseudonymization
• Data Generalization
• Data Swapping
• Data Perturbation
• Data Synthetic
• Data Shuffling
• Data Scrambling
• Data Encoding
• Data De-identification

Data Identification is the pre-requisite for Data Anonymization. How?

Data Identification is opposite to Data Anonymization, in other words, first one must identify data before conducting Data Anonymization.

Data Identification is about those set of details which assist in identifying an entity or individual. These details can be direct details like name, contact number, address, credit card number etc. or can be indirect details like unique identification number which assist to connect with other datasets to identify an entity or individual.

Data Identification is a very crucial exercise in data migration project where the process is to first conduct data discovery then data identification and then data migration can take place.

Data Identification can be done in all kinds of datasets i.e., structure, semi or unstructured but recommended practice is to do it in structured datasets.

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