Cluster Analysis in Data Mining: The Million-Dollar Pattern in Data

Cluster analysis in data mining is a technique used to group similar data points into clusters, helping to identify patterns and structures within large datasets. By analyzing the inherent relationships between data points, it enables the discovery of hidden groupings without prior knowledge of the data's labels.

Common methods of cluster analysis include K-Means, hierarchical clustering, and DBSCAN. Each method has its strengths and is chosen based on the nature of the data and the specific requirements of the analysis.

In this blog, you'll explore the fundamentals of cluster analysis in data mining, including various methods, benefits, applications, limitations and more.

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What Is Cluster Analysis in Data Mining and Why Is It Crucial?

A cluster is a set of items that share certain features or behaviors. By grouping these items, you can spot patterns that might stay hidden if you treat each one separately. Cluster analysis in data mining builds on this idea by forming groups (clusters) without predefined labels.

It uses similarities between data points to highlight relationships that would be hard to see in a cluttered dataset, making it easier to understand massive datasets with no predefined labels.

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Let’s take an example to understand cluster analysis in data mining better.

Imagine you run an online learning platform with thousands of learners. You collect various types of data about how these learners interact with your platform:

• Some learners prefer watching short video tutorials at their own pace.
• Others log in daily to attempt practice tests, focusing on improving their knowledge.
• A few learners prefer live sessions where they can interact with mentors and ask questions in real-time.

By applying cluster analysis, you can group these learners based on their study habits and behaviors. For instance:

• One cluster might consist of learners who mainly watch videos and prefer self-paced learning.
• Another cluster could include learners who focus on daily practice tests and need personalized assessments or feedback.
• A third cluster might be made up of learners who attend live sessions, indicating a need for more interactive, mentor-driven support.

With these clusters, you can:

• Create targeted course plans tailored to each group (e.g., video-based content for self-paced learners, practice tests for test-focused learners, and live sessions for mentor-driven learners).
• Streamline the user experience by offering the right learning resources to each group, improving engagement and satisfaction.  

This helps you deliver focused support without sorting through heaps of data one record at a time.

Also Read: Cluster Analysis in Business Analytics: Everything to know

Importance of Cluster Analysis in Data Mining

As datasets grow, it becomes tough to see everything at once. Cluster analysis in data mining solves this by breaking down information into smaller, more uniform groups. This approach highlights connections that might remain hidden, supports decisions with data-driven insights, and saves time when you need to act on real trends.

Here are the key reasons why clustering in data mining is so important:

• It organizes unstructured data into manageable segments
• It reveals relationships that simple sorting often misses
• It applies to many tasks, such as customer research or anomaly detection
• It simplifies your workflow, even when dealing with different types of data

Real World Example:
Using cluster analysis, Amazon segments its customers based on purchasing behavior, browsing patterns, and demographic data. This allows Amazon to recommend personalized products to different customer groups, improving user experience and increasing sales.

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Also Read: Cluster Analysis in R: A Complete Guide You Will Ever Need

Next, let’s look at some of the key properties underlining cluster analysis in data mining.

Key Properties Underlying Cluster Analysis in Data Mining

Clustering in data mining rests on certain ideas that shape how data points are gathered into meaningful groups. Each cluster aims to pull together points that share important traits while keeping dissimilar points apart. This may sound simple, but some nuances help you decide if your groups make sense.

Key Considerations:

• A key consideration is how closely items in a cluster resemble each other compared to items in other clusters.
• Another is whether clusters stand apart clearly enough for you to draw useful conclusions.

When these aspects are handled well, cluster analysis results can guide decisions and uncover patterns you might otherwise miss.
Here are the four properties that form the backbone of a strong clustering setup:

• Homogeneity: It shows how much the points in a group share specific features.
• Separation: It measures how clearly a group stands out from others.
• Compactness: It tells you if points in the same group stay close together.
• Connectedness: It checks how strongly each point belongs within its group.

If these properties of clustering all hold together, your clusters stand a better chance of revealing trends you can trust.

You will learn more about clustering techniques with upGrad’s free Unsupervised Learning: Clustering course. Explore K-Means, Hierarchical Clustering, and practical applications to uncover hidden patterns in unlabelled data.

Also Read: Clustering in Machine Learning: Learn About Different Techniques and Applications

Now that you’re familiar with the significance of cluster analysis in data mining, let’s look at some of the main clustering methods in data mining.

What Are the 7 Main Cluster Analysis Methods in Data Mining?

When you set out to group data points, you have a range of well-known clustering methods in data mining at your disposal. Each one differs in how it draws boundaries and adapts to your dataset. Some methods split your data into a fixed number of groups, while others discover clusters based on density or probabilistic models. 

Knowing these options will help you pick what fits your goals and the nature of your data.

1. Partitioning Method

The partitioning method divides data into non-overlapping clusters so that each data point belongs to only one cluster. It is suitable for datasets with clearly defined, separate clusters.

 K-Means is a common example. It starts by choosing cluster centers and then refines them until each data point is close to its center. This method is quick to run but needs you to guess how many clusters work best.

Example:

Suppose you’re analyzing student attendance (in hours per week) and test scores (percentage) to see if there are two clear groups. You want to check if some students form a group that needs more help while others seem to be doing fine.

Here, k-means tries to form exactly two clusters. 

• The “centers” tell each group's average attendance and test score.
• Students labelled "0" might need extra support, whereas "1" might be the more comfortable group.

Case Study - Netflix:

Netflix uses K-Means clustering to segment users based on their viewing habits. By grouping users with similar interests (e.g., "action movie lovers," "comedy fans"), Netflix can provide more accurate content recommendations and improve user engagement.

import numpy as np
from sklearn.cluster import KMeans

# [attendance_hours_per_week, test_score_percentage]
X = np.array([
    [3, 40], [4, 45], [2, 38],
    [10, 85], [11, 80], [9, 90]
])

kmeans = KMeans(n_clusters=2, random_state=0)
kmeans.fit(X)

print("Cluster Centers:", kmeans.cluster_centers_)
print("Labels:", kmeans.labels_)

Expected Output:

The KMeans algorithm splits the data into two clusters. Given that the data points seem to fall into two groups, one with lower attendance and scores and another with higher scores and attendance.

Cluster Centers: [[ 3.        44.33333333]

                  [10.        85.        ]]

Labels: [0 0 0 1 1 1]

2. Hierarchical Method

A hierarchical algorithm builds clusters in layers. One approach starts with each data point on its own, merging them step by step until everything forms one large group. Another starts with a single group and keeps splitting it. 

You end up with a tree-like view, which shows how clusters connect or differ at various scales. It’s easy to visualize but can slow down with very large datasets.

Example:

You might record daily study hours and daily online forum interactions for a set of learners. You’re curious if a natural layering or grouping emerges, such as one big group that subdivides into smaller clusters.

• The algorithm starts with each point alone and merges them until only two groups remain. 
• You can look at the final labels to see which learners ended up together. 
• A dendrogram (if you visualize it) would show how these merges happened at each step.

Case Study - LinkedIn:

LinkedIn uses hierarchical clustering to detect professional networks and job recommendation patterns. By analyzing users' profiles and connections, the algorithm builds a dendrogram to understand professional relationships and make more accurate job suggestions.

import numpy as np
from sklearn.cluster import AgglomerativeClustering

# [study_hours, forum_interactions_per_day]
X = np.array([
    [1, 2], [1, 3], [2, 2],
    [5, 10], [6, 9], [5, 11]
])

agglo = AgglomerativeClustering(n_clusters=2, linkage='ward')
labels = agglo.fit_predict(X)
print("Labels:", labels)

Expected Output:

The Agglomerative Clustering method merges the points step by step into clusters. With n_clusters=2, the expected output will group the points into two clusters based on their similarities.

Labels: [1 1 1 0 0 0]

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Also Read: Understanding the Concept of Hierarchical Clustering in Data Analysis: Functions, Types & Steps

3. Density-based Method

The density-based method allows you to identify clusters as dense regions in data, effectively handling noise and outliers. Clusters are formed where data points are closely packed together, separated by areas of lower data density. It can be effectively used for irregularly shaped clusters and noisy data.

DBSCAN is a well-known example. It places points together if they pack closely, labeling scattered points as outliers. You don’t need to pick a cluster number, but you do set parameters that define density. This method captures odd-shaped groups and handles noisy data well.

Example: 

Suppose you track weekly code submissions and average accuracy. Some learners cluster around moderate submission counts, while a few show very high accuracy with fewer submissions.

• DBSCAN looks for dense pockets where points sit close together in terms of submissions and accuracy. 
• The “eps=3” setting decides how close points must be, and “min_samples=2” means at least two points need to be within that distance. 
• Points that don’t meet those rules get a label like “-1,” marking them as outliers.

Case Study - Credit Card Fraud Detection:

Credit card companies use DBSCAN to detect anomalous spending patterns. By analyzing transactions, DBSCAN identifies unusual behaviors, such as a sudden large purchase in a new location, which may indicate fraud.

import numpy as np
from sklearn.cluster import DBSCAN

# [weekly_submissions, average_accuracy_percentage]
X = np.array([
    [3, 50], [4, 55], [5, 60],
    [10, 85], [11, 87], [9, 83],
    [20, 95]  # might be an outlier or a separate cluster
])

dbscan = DBSCAN(eps=3, min_samples=2)
labels = dbscan.fit_predict(X)
print("Labels:", labels)

Expected Output:

DBSCAN identifies dense regions and labels outliers. In this case, the data has a clear group with one outlier at [20, 95], so DBSCAN will mark it as an outlier (label -1).

Labels: [ 0  0  0  1  1  1 -1]

4. Grid-based Method

Here, you divide the data space into cells, like squares on a grid. Then, you check how dense each cell is, merging those that touch and share similar density. By focusing on the cells instead of every single point, this method can work quickly on very large datasets. 

It’s often chosen for spatial data or cases where you want a broad view of how points cluster together.

Example: 

the code maps each point to a cell. Each cell is two units wide. Once cells fill up with enough points, they could be merged if they sit next to cells with similar densities. This script shows a simple idea of splitting the space into cells.

Case Study - Urban Planning:

City planners use grid-based clustering to optimize resource placement, such as hospitals, schools, and transportation systems. By analyzing population density, they can allocate resources to high-demand areas more effectively.

import numpy as np

X = np.array([
    [1, 2], [1, 3], [2, 2],
    [8, 7], [8, 8], [7, 8],
    [3, 2], [4, 2]
])

grid_size = 2
cells = {}

# Assign points to cells based on integer division
for x_val, y_val in X:
    x_cell = int(x_val // grid_size)
    y_cell = int(y_val // grid_size)
    cells.setdefault((x_cell, y_cell), []).append((x_val, y_val))

clusters = []
for cell, points in cells.items():
    clusters.append(points)

print("Grid Cells:", cells)
print("Total Clusters (basic grouping):", len(clusters))

Expected Output:

The code divides the space into grid cells based on the given grid_size and assigns the points to the corresponding cells.

Grid Cells: {(0, 1): [(1, 2), (1, 3), (2, 2)], (3, 3): [(8, 7), (8, 8), (7, 8)], (1, 1): [(3, 2), (4, 2)]}

Total Clusters: 3

5. Model-based Method

In model-based clustering in data mining, you assume data follows certain statistical patterns, such as Gaussian distributions. The algorithm estimates these distributions and assigns points to the model that fits best. 

This works well when you believe your data naturally falls into groups of known shapes, though it might struggle if the real patterns differ from those assumptions.

Example: 

This snippet fits two Gaussian distributions to the data. It then assigns each point to whichever distribution provides the best fit. You see the mean of each distribution and how each point is labeled.

Case Study - Customer Segmentation:

Retail companies use GMM to segment their customers into different categories based on purchasing behavior. By identifying different Gaussian distributions, businesses can optimize marketing campaigns for each group, increasing conversion rates.

import numpy as np
from sklearn.mixture import GaussianMixture

X = np.array([
    [1, 2], [2, 2], [1, 3],
    [8, 7], [8, 8], [7, 7]
])

gmm = GaussianMixture(n_components=2, random_state=0)
gmm.fit(X)
labels = gmm.predict(X)

print("Means:", gmm.means_)
print("Labels:", labels)

Expected Output:

The Gaussian Mixture Model assumes two Gaussian distributions. It will classify the data points based on which distribution (cluster) they best fit.

Means: [[1.33333333 2.33333333]

        [7.66666667 7.66666667]]

Labels: [0 0 0 1 1 1]

Also Read: Gaussian Naive Bayes: Understanding the Algorithm and Its Classifier Applications

6. Constraint-based Method

If you have rules that define how clusters must form, constraint-based methods let you apply them. These rules might involve distances, capacity limits, or domain-specific criteria. This approach gives you more control over the final groups, though it can be tricky if your constraints are too strict or your data doesn’t follow simple rules.

Example:

Suppose you run an online test series for a small group, and you want to ensure that no cluster has fewer than three learners, as smaller groups would not provide useful insights.This snippet modifies K-Means to respect a minimum size.

• The code attempts to form two clusters but checks if any cluster has fewer than three points. 
• If so, it repositions that cluster’s center and tries again until the rule is met or it reaches the maximum number of attempts.

Case Study - E-commerce:

In e-commerce, constraint-based clustering can be used to group customers while ensuring that each segment has a minimum number of customers, allowing for effective targeted marketing campaigns.

import numpy as np
from sklearn.cluster import KMeans

def constrained_kmeans(data, k, min_size=3, max_iter=5):
    model = KMeans(n_clusters=k, random_state=0)
    for _ in range(max_iter):
        labels = model.fit_predict(data)
        counts = np.bincount(labels)
        if all(count >= min_size for count in counts):
            return labels, model.cluster_centers_
        for idx, size in enumerate(counts):
            if size < min_size:
                # Move this center so that cluster tries again
                model.cluster_centers_[idx] = np.random.uniform(
                    np.min(data, axis=0),
                    np.max(data, axis=0)
                )
    return labels, model.cluster_centers_

X = np.array([
    [2, 2], [1, 2], [2, 1],
    [6, 8], [7, 9], [5, 7],
    [2, 3]
])

labels, centers = constrained_kmeans(X, k=2)
print("Labels:", labels)
print("Centers:", centers)

Expected Output:

The function modifies K-Means to ensure that each cluster contains at least min_size points. The output will include labels and cluster centers that satisfy this constraint.

Labels: [1 1 1 0 0 0 1]

Centers: [[6. 8.]

          [2. 2.]]

7. Fuzzy Clustering

Most clustering methods make a point belonging to exactly one cluster. Fuzzy clustering, on the other hand, allows a point to belong to several clusters with different levels of membership. 

This is useful when data points share features across groups or when you suspect strict boundaries don’t capture the full story. You can fine-tune how strongly a point belongs to each group, which can give you a more nuanced understanding of overlapping patterns.

Example: 

A set of learners might rely partly on recorded lectures and partly on live sessions. Instead of forcing them into a single group, you assign them to both with different strengths.

• Here, each learner may have partial membership in both clusters. 
• If a learner’s membership matrix is [0.4, 0.6], it means they’re partly in the first group but even more aligned with the second group.

Case Study - Marketing:

In marketing, fuzzy clustering can be used to segment customers who may belong to multiple categories, such as “budget-conscious” and “brand-loyal.” This approach allows more tailored messaging and product recommendations.

!pip install fcmeans  # Install once in your environment
import numpy as np
from fcmeans import FCM

# [hours_recorded_lectures, hours_live_sessions]
X = np.array([
    [2, 0.5], [2, 1], [3, 1.5],
    [8, 3], [7, 2.5], [9, 4]
])

fcm = FCM(n_clusters=2)
fcm.fit(X)
labels = fcm.predict(X)
membership = fcm.u

print("Labels:", labels)
print("Membership Degrees:\n", membership)

Expected Output:

Fuzzy clustering will assign each point partial membership in both clusters. The output will show the labels as well as the membership degrees for each data point.

Labels: [0 0 0 1 1 1]

Membership Degrees:

 [[0.78821994 0.21178006]

 [0.70710171 0.29289829]

 [0.47654857 0.52345143]

 [0.14448534 0.85551466]

 [0.23431997 0.76568003]

 [0.05101742 0.94898258]]

How to Choose the Right Clustering Method for Your Data?

Picking a suitable clustering approach is key to getting reliable results. The method you use should match the size and shape of your data, along with the goals you have in mind.

Before you decide, weigh the following points:

• Data Shape and Distribution: A partitioning method like K-Means may work well if your data forms spherical groups. For more complex or elongated shapes, consider density-based or hierarchical approaches.
• Number of Clusters: Some methods need you to specify a cluster count beforehand, while others (like DBSCAN) find clusters on their own. Think about whether you have a solid estimate of how many groups exist.
• Handling Outliers and Noise: Density-based methods can handle scattered points better than basic partitioning. If your dataset has lots of anomalies, they may be a better fit.
• Scalability: Check if the algorithm can handle a large number of data points in a reasonable time. Methods like K-Means often run faster, whereas hierarchical approaches can slow down if you have thousands of points.
• Interpretability: If you need to explain why data points form certain groups, hierarchical methods give you a visual tree structure. Meanwhile, model-based methods use statistical reasoning that may be clear if you have relevant domain knowledge.
• Available Resources: Consider your computing limits. Some approaches might require more memory or processing power than others, especially if your dataset is extensive.

Also Read: Explanatory Guide to Clustering in Data Mining - Definition, Applications & Algorithms

Before you perform cluster analysis, you will need to prepare data so it is optimized for effective results.

How Do You Prepare Data for Effective Cluster Analysis?

A well-prepared dataset lays the groundwork for useful results. If your data has too many missing values or relies on mismatched scales, your clustering model could group points for the wrong reasons. 

By focusing on good data hygiene, removing bad entries, choosing the right features, and keeping everything on a fair scale, you give your algorithm a reliable starting point. This way, any patterns you find are more likely to reflect actual relationships instead of noise or inconsistent units.

Key Steps to Get Your Data Ready:

• Clean Out Missing and Erroneous Entries: Look for rows or columns with missing values, obvious errors, or unlikely numbers. Decide whether to fix them (for instance, by using an average) or remove them altogether. This step prevents random gaps or faulty inputs from throwing your clusters off.
• Scale Your Features: If one column ranges from 1 to 10 and another goes from 1 to 1,000, the larger range might overshadow everything else. Normalizing or standardizing each feature ensures every attribute has a similar impact on the final clusters.
• Handle Outliers Carefully: Strong outliers can skew distance-based calculations. You can examine whether these points are genuine (and thus noteworthy) or simply errors. If they’re valid but too extreme, consider applying transformations like log scaling to soften their effect.
• Choose Relevant Features: Not every column helps the clustering process. Too many irrelevant features can bury the real relationships. A good mix of domain knowledge and exploratory analysis helps you keep the attributes that matter.
• Convert Categorical Data: Certain clustering methods need numeric inputs. You can apply techniques like one-hot encoding for data in text or categorical form. This turns categories into 0-or-1 signals, allowing algorithms to process them effectively.
• Double-Check Consistency: Different data sources might store information in incompatible formats. Check for things like date formats, labels, or regional decimal marks. Make sure all items follow the same rules so they can be compared evenly.

Following these steps puts you on firmer ground. Instead of grappling with disorganized data, your clusters emerge from well-structured information. This boosts the odds that your final insights will be accurate and meaningful.

Also Read: K Means Clustering in R: Step by Step Tutorial with Example

How Can Clustering Results Be Validated and Evaluated?

Once you build clusters, you must check if they represent meaningful groups. Validation helps confirm that your chosen method hasn’t formed accidental patterns or ignored important details.

Below are the main ways to measure your clusters' performance and suggestions for using these insights in practice.

1. Judging Cluster Performance Through Internal Validation

Internal methods rely only on the data and the clustering itself. They judge how cohesive each cluster is and whether different clusters stand apart clearly.

Here are the most relevant methods:

• Silhouette Coefficient: Looks at how close points are to others in their group compared to points in neighboring groups. A higher silhouette value (close to 1) suggests cleaner clusters.
• Davies–Bouldin Index: Examines how clusters compare to each other based on their average distance within and between groups. A lower value indicates well-separated clusters.
• Dunn Index: Focuses on the ratio of the smallest distance between any two clusters to the largest distance within a single cluster. A higher score usually means stronger separation and consistency.

Transitioning to external checks is important when you have labels or extra information that you can compare against these internally formed clusters.

2. Judging Cluster Performance Through External Validation

Here, you compare your clusters to existing labels or categories in the data. External methods – listed below – measure how your unsupervised groups match up with known groupings.

• Adjusted Rand Index: Evaluates how closely your clusters align with a labeled set. It corrects for random chance, so you can see if your results are better than guessing.
• Normalized Mutual Information: Checks how much you gain by knowing both your clusters and the actual labels. A higher value shows a stronger overlap between the two sets.
• Fowlkes–Mallows Index: Balances how precisely you formed each cluster and how completely you captured each true category. It’s another metric that tells you if your results align with existing labels.

Once you confirm your clusters match or explain real categories, you can apply the following practical steps to refine them further:

• Use Multiple Metrics: Check at least two or three different scores instead of relying on just one. Different measures emphasize different facets of cluster quality.
• Visualize Your Results: Charts like scatter plots (for 2D or 3D data) or dendrograms (for hierarchical methods) help you see if your clusters make sense. They also reveal whether points are scattered or packed together.
• Experiment with Parameters: If you suspect your current settings aren’t optimal, adjust things like the number of clusters or density thresholds. Follow up with the same validation measures to see if there’s an improvement.

By monitoring these metrics and refining your method as needed, you end up with clusters that are easier to trust and explain.

Also Read: Data Cleaning Techniques: Learn Simple & Effective Ways To Clean Data

Benefits and Limitations of Cluster Analysis in Data Mining

Cluster analysis in data mining is a powerful tool that helps simplify complex datasets by grouping similar items together. However, while clustering can reveal valuable insights, it’s not without its limitations.

It’s essential to understand when clustering is appropriate and where it may fall short, so you can apply it effectively or consider alternative techniques.

Here’s a comparison of the benefits and limitations of clustering:

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Also Read: What is DBSCAN Clustering? Key Concepts, Implementation & Applications

Next, let’s look at some real-life applications of cluster analysis in data mining across industries.

Where Do You See Clustering in Data Mining in Practical Applications?

Clustering in data mining shines in areas where you handle diverse data and need to group items that share common traits. Whether you’re segmenting customers for focused marketing or spotting sudden shifts in large networks, this method finds natural patterns in the data. 

Below is a snapshot of how different sectors put clustering into action:

Also Read: 25+ Real-World Data Mining Examples That Are Transforming Industries

What Are the Future Directions of Cluster Analysis in Data Mining?

Cluster analysis in data mining has come a long way, with fresh ideas that tackle bigger datasets and more varied patterns. Researchers and data experts now try approaches that go beyond standard algorithms, drawing on concepts from real-time data processing, and even specialized hardware.

These efforts aim to make clustering both faster and more adaptable to the problems you face.

• Deep Clustering Techniques: Neural networks can compress and restructure data before grouping it, making it possible to discover subtle patterns. Autoencoders, for instance, learn an internal representation that reveals shapes simple methods might miss.
• Online and Streaming Data: Some methods handle incoming data points on the fly, updating clusters without waiting for a full batch. This keeps clusters accurate in situations where new information never stops flowing.
• Distributed and Parallel Methods: When data grows beyond a single system’s capacity, clustering can split tasks across multiple machines. This speeds up the process and allows you to scale your computations without running into hardware limits.
• Domain-Specific Refinements: Clustering approaches that align with industry needs like more advanced distance measures or specialized constraints ,continue to pop up. This custom focus can highlight patterns that generic algorithms often overlook.

Also Read: What is Centroid-Based Clustering? Implementation, Variations & Applications

Now that you know how to use cluster analysis in data mining, let’s look at how upGrad can help you in your learning journey.

How upGrad Can Help You Master Cluster Analysis in Data Mining?

Cluster analysis in data mining identifies patterns and groupings within datasets, driving informed decision-making. Using cluster analysis methods, professionals can gain actionable insights, refine strategies, and solve complex problems across industries, boosting both analytical skills and practical outcomes.

To master cluster analysis in data mining and make an exciting career in this growing field, upGrad offers comprehensive programs that provide hands-on experience with advanced technology.

In addition to the courses mentioned above, here are some free courses that can further strengthen your foundation in Data Science and AI:

• Case Study Using Tableau, Python and SQL
• Data Science in E-Commerce

Here are some of upGrad’s courses related to data mining:

• Introduction to Data Analysis using Excel
• Data Structures & Algorithms

Need further help deciding which courses can help you excel in data mining? Contact upGrad for personalized counseling to choose the best path tailored to your goals. You can also visit your nearest upGrad center and start hands-on training today!

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• Top 30 Data Mining Projects Ideas: From Beginner to Expert
• Key Data Mining Functionalities with Examples for Better Analysis
• Clustering vs Classification: Difference Between Clustering & Classification
• Clustering in Machine Learning: Learn About Different Techniques and Applications

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