Clustering in Machine Learning: The Techniques & Analysis in Data Mining
How do machine learning professionals make data readable and accessible? What techniques do they use to dissect raw information?
One of these techniques is clustering. Data clustering is the process of grouping items in a data set together. These items are related, allowing key stakeholders to make critical strategic decisions using the insights.
After preparing data, which is what specialists do 50%-80% of the time, clustering takes center stage. It forms structures other members of the company can understand more easily, even if they lack advanced technical knowledge.
Clustering in machine learning involves many techniques to help accomplish this goal. Here is a detailed overview of those techniques.
Data science is an ever-changing field with lots of variables and fluctuations. However, one thing’s for sure – whether you want to practice clustering in data mining or clustering in machine learning, you can use a wide array of tools to automate your efforts.
The first groups of techniques are the so-called partitioning methods. There are three main sub-types of this model.
K-means clustering is an effective yet straightforward clustering system. To execute this technique, you need to assign clusters in your data sets. From there, define your number K, which tells the program how many centroids (“coordinates” representing the center of your clusters) you need. The machine then recognizes your K and categorizes data points to nearby clusters.
You can look at K-means clustering like finding the center of a triangle. Zeroing in on the center lets you divide the triangle into several areas, allowing you to make additional calculations.
And the name K-means clustering is pretty self-explanatory. It refers to finding the median value of your clusters – centroids.
K-means clustering is useful but is prone to so-called “outlier data.” This information is different from other data points and can merge with others. Data miners need a reliable way to deal with this issue.
Enter K-medoids clustering.
It’s similar to K-means clustering, but just like planes overcome gravity, so does K-medoids clustering overcome outliers. It utilizes “medoids” as the reference points – which contain maximum similarities with other data points in your cluster. As a result, no outliers interfere with relevant data points, making this one of the most dependable clustering techniques in data mining.
Fuzzy C-Means Clustering
Fuzzy C-means clustering is all about calculating the distance from the median point to individual data points. If a data point is near the cluster centroid, it’s relevant to the goal you want to accomplish with your data mining. The farther you go from this point, the farther you move the goalpost and decrease relevance.
Some forms of clustering in machine learning are like textbooks – similar topics are grouped in a chapter and are different from topics in other chapters. That’s precisely what hierarchical clustering aims to accomplish. You can the following methods to create data hierarchies.
Agglomerative clustering is one of the simplest forms of hierarchical clustering. It divides your data set into several clusters, making sure data points are similar to other points in the same cluster. By grouping them, you can see the differences between individual clusters.
Before the execution, each data point is a full-fledged cluster. The technique helps you form more clusters, making this a bottom-up strategy.
Divisive clustering lies on the other end of the hierarchical spectrum. Here, you start with just one cluster and create more as you move through your data set. This top-down approach produces as many clusters as necessary until you achieve the requested number of partitions.
Birds of a feather flock together. That’s the basic premise of density-based methods. Data points that are close to each other form high-density clusters, indicating their cohesiveness. The two primary density-based methods of clustering in data mining are DBSCAN and OPTICS.
DBSCAN (Density-Based Spatial Clustering of Applications With Noise)
Related data groups are close to each other, forming high-density areas in your data sets. The DBSCAN method picks up on these areas and groups information accordingly.
OPTICS (Ordering Points to Identify the Clustering Structure)
The OPTICS technique is like DBSCAN, grouping data points according to their density. The only major difference is that OPTICS can identify varying densities in larger groups.
You can see grids on practically every corner. They can easily be found in your house or your car. They’re also prevalent in clustering.
STING (Statistical Information Grid)
The STING grid method divides a data point into rectangular grills. Afterward, you determine certain parameters for your cells to categorize information.
CLIQUE (Clustering in QUEst)
Agglomerative clustering isn’t the only bottom-up clustering method on our list. There’s also the CLIQUE technique. It detects clusters in your environment and combines them according to your parameters.
Different clustering techniques have different assumptions. The assumption of model-based methods is that a model generates specific data points. Several such models are used here.
Gaussian Mixture Models (GMM)
The aim of Gaussian mixture models is to identify so-called Gaussian distributions. Each distribution is a cluster, and any information within a distribution is related.
Hidden Markov Models (HMM)
Most people use HMM to determine the probability of certain outcomes. Once they calculate the probability, they can figure out the distance between individual data points for clustering purposes.
If you often deal with information organized in graphs, spectral clustering can be your best friend. It finds related groups of notes according to linked edges.
Comparison of Clustering Techniques
It’s hard to say that one algorithm is superior to another because each has a specific purpose. Nevertheless, some clustering techniques might be especially useful in particular contexts:
- OPTICS beats DBSCAN when clustering data points with different densities.
- K-means outperforms divisive clustering when you wish to reduce the distance between a data point and a cluster.
- Spectral clustering is easier to implement than the STING and CLIQUE methods.
You can’t put your feet up after clustering information. The next step is to analyze the groups to extract meaningful information.
Importance of Cluster Analysis in Data Mining
The importance of clustering in data mining can be compared to the importance of sunlight in tree growth. You can’t get valuable insights without analyzing your clusters. In turn, stakeholders wouldn’t be able to make critical decisions about improving their marketing efforts, target audience, and other key aspects.
Steps in Cluster Analysis
Just like the production of cars consists of many steps (e.g., assembling the engine, making the chassis, painting, etc.), cluster analysis is a multi-stage process:
Noise and other issues plague raw information. Data preprocessing solves this issue by making data more understandable.
You zero in on specific features of a cluster to identify those clusters more easily. Plus, feature selection allows you to store information in a smaller space.
Clustering Algorithm Selection
Choosing the right clustering algorithm is critical. You need to ensure your algorithm is compatible with the end result you wish to achieve. The best way to do so is to determine how you want to establish the relatedness of the information (e.g., determining median distances or densities).
In addition to making your data points easily digestible, you also need to verify whether your clustering process is legit. That’s where cluster validation comes in.
Cluster Validation Techniques
There are three main cluster validation techniques when performing clustering in machine learning:
Internal validation evaluates your clustering based on internal information.
External validation assesses a clustering process by referencing external data.
You can vary your number of clusters or other parameters to evaluate your clustering. This procedure is known as relative validation.
Applications of Clustering in Data Mining
Clustering may sound a bit abstract, but it has numerous applications in data mining.
- Customer Segmentation – This is the most obvious application of clustering. You can group customers according to different factors, like age and interests, for better targeting.
- Anomaly Detection – Detecting anomalies or outliers is essential for many industries, such as healthcare.
- Image Segmentation – You use data clustering if you want to recognize a certain object in an image.
- Document Clustering – Organizing documents is effortless with document clustering.
- Bioinformatics and Gene Expression Analysis – Grouping related genes together is relatively simple with data clustering.
Challenges and Future Directions
- Scalability – One of the biggest challenges of data clustering is expected to be applying the process to larger datasets. Addressing this problem is essential in a world with ever-increasing amounts of information.
- Handling High-Dimensional Data – Future systems may be able to cluster data with thousands of dimensions.
- Dealing with Noise and Outliers – Specialists hope to enhance the ability of their clustering systems to reduce noise and lessen the influence of outliers.
- Dynamic Data and Evolving Clusters – Updates can change entire clusters. Professionals will need to adapt to this environment to retain efficiency.
Elevate Your Data Mining Knowledge
There are a vast number of techniques for clustering in machine learning. From centroid-based solutions to density-focused approaches, you can take many directions when grouping data.
Mastering them is essential for any data miner, as they provide insights into crucial information. On top of that, the data science industry is expected to hit nearly $26 billion by 2026, which is why clustering will become even more prevalent.
Soon, we will be launching four new Degrees for AY24-25 at OPIT – Open Institute of Technology
I want to offer a behind-the-scenes look at the Product Definition process that has shaped these upcoming programs.
🚀 Phase 1: Discovery (Late May – End of July)
Our journey began with intensive brainstorming sessions with OPIT’s Academic Board (Francesco Profumo, Lorenzo Livi, Alexiei Dingli, Andrea Pescino, Rosario Maccarrone) . We also conducted 50+ interviews with tech and digital entrepreneurs (both from startups and established firms), academics and students. Finally, we deep-dived into the “Future of Jobs 2023” report by the World Economic Forum and other valuable research.
🔍 Phase 2: Selection – Crafting Our Roadmap (July – August)
Our focus? Introducing new degrees addressing critical workforce shortages and upskilling/reskilling needs for the next 5-10 years, promising significant societal impact and a broad market reach.
Our decision? To channel our energies on full BScs and MScs, and steer away from shorter courses or corporate-focused offerings. This aligns perfectly with our core mission.
💡 Focus Areas Unveiled!
We’re thrilled to concentrate on pivotal fields like:
- Advanced AI
- Digital Business
- Metaverse & Gaming
- Cloud Computing (less “glamorous”, but market demand is undeniable).
🎓 Phase 3: Definition – Shaping the Degrees (August – November)
With an expert in each of the above fields, and with the strong collaboration of our Academic Director, Prof. Lorenzo Livi , we embarked on a rigorous “drill-down process”. Our goal? To meld modern theoretical knowledge with cutting-edge competencies and skills. This phase included interviewing over 60+ top academics, industry professionals, and students and get valuable, program-specific, insights from our Marketing department.
🌟 Phase 4: Accreditation and Launch – The Final Stretch
We’re currently in the accreditation process, gearing up for the launch. The focus is now shifting towards marketing, working closely with Greta Maiocchi and her Marketing and Admissions team. Together, we’re translating our new academic offering into a compelling value proposition for the market.
Stay tuned for more updates!
Far from being a temporary educational measure that came into its own during the pandemic, online education is providing students from all over the world with new ways to learn. That’s proven by statistics from Oxford Learning College, which point out that over 100 million students are now enrolled in some form of online course.
The demand for these types of courses clearly exists.
In fact, the same organization indicates that educational facilities that introduce online learning see a 42% increase in income – on average – suggesting that the demand is there.
Enter the Open Institute of Technology (OPIT).
Delivering three online courses – a Bachelor’s degree in computer science and two Master’s degrees – with more to come, OPIT is positioning itself as a leader in the online education space. But why is that? After all, many institutions are making the jump to e-learning, so what separates OPIT from the pack?
Here, you’ll discover the answers as you delve into the five reasons why you should trust OPIT for your online education.
Reason 1 – A Practical Approach
OPIT focuses on computer science education – a field in which theory often dominates the educational landscape. The organization’s Rector, Professor Francesco Profumo, makes this clear in a press release from June 2023. He points to a misalignment between what educators are teaching computer science students and what the labor market actually needs from those students as a key problem.
“The starting point is the awareness of the misalignment,” he says when talking about how OPIT structures its online courses. “That so-called mismatch is generated by too much theory and too little practical approach.” In other words, students in many classes spend far too much time learning the “hows” and “whys” behind computerized systems without actually getting their hands dirty with real work that gives them practical experience in using those systems.
OPIT takes a different approach.
It has developed a didactic approach that focuses far more on the practical element than other courses. That approach is delivered through a combination of classroom sessions – such as live lessons and masterclasses – and practical work offered through quizzes and exercises that mimic real-world situations.
An OPIT student doesn’t simply learn how computers work. They put their skills into practice through direct programming and application, equipping them with skills that are extremely attractive to major employers in the tech field and beyond.
Reason 2 – Flexibility Combined With Support
Flexibility in how you study is one of the main benefits of any online course.
You control when you learn and how you do it, creating an environment that’s beneficial to your education rather than being forced into a classroom setting with which you may not feel comfortable. This is hardly new ground. Any online educational platform can claim that it offers “flexibility” simply because it provides courses via the web.
Where OPIT differs is that it combines that flexibility with unparalleled support bolstered by the experiences of teachers employed from all over the world. The founder and director of OPIT, Riccardo Ocleppo, sheds more light on this difference in approach when he says, “We believe that education, even if it takes place physically at a distance, must guarantee closeness on all other aspects.” That closeness starts with the support offered to students throughout their entire study period.
Tutors are accessible to students at all times. Plus, every participant benefits from weekly professor interactions, ensuring they aren’t left feeling stuck on an educational “island” and have to rely solely on themselves for their education. OPIT further counters the potential isolation that comes with online learning with a Student Support team to guide students through any difficulties they may have with their courses.
In this focus on support, OPIT showcases one of its main differences from other online platforms.
You don’t simply receive course material before being told to “get on with it.” You have the flexibility to learn at your own pace while also having a support structure that serves as a foundation for that learning.
Reason 3 – OPIT Can Adapt to Change Quickly
The field of computer science is constantly evolving.
In the 2020s alone, we’ve seen the rise of generative AI – spurred on by the explosive success of services like ChatGPT – and how those new technologies have changed the way that people use computers.
Riccardo Ocleppo has seen the impact that these constant evolutions have had on students. Before founding OPIT, he was an entrepreneur who received first-hand experience of the fact that many traditional educational institutions struggle to adapt to change.
“Traditional educational institutions are very slow to adapt to this wave of new technologies and trends within the educational sector,” he says. He points to computer science as a particular issue, highlighting the example of a board in Italy of which he is a member. That board – packed with some of the country’s most prestigious tech universities – spent three years eventually deciding to add just two modules on new and emerging technologies to their study programs.
That left Ocleppo feeling frustrated.
When he founded OPIT, he did so intending to make it an adaptable institution in which courses were informed by what the industry needs. Every member of its faculty is not only a superb teacher but also somebody with experience working in industry. Speaking of industry, OPIT collaborates with major companies in the tech field to ensure its courses deliver the skills that those organizations expect from new candidates.
This confronts frustration on both sides. For companies, an OPIT graduate is one for which they don’t need to bridge a “skill gap” between what they’ve learned and what the company needs. For you, as a student, it means that you’re developing skills that make you a more desirable prospect once you have your degree.
Reason 4 – OPIT Delivers Tier One Education
Despite their popularity, online courses can still carry a stigma of not being “legitimate” in the face of more traditional degrees. Ocleppo is acutely aware of this fact, which is why he’s quick to point out that OPIT always aims to deliver a Tier One education in the computer science field.
“That means putting together the best professors who create superb learning material, all brought together with a teaching methodology that leverages the advancements made in online teaching,” he says.
OPIT’s degrees are all accredited by the European Union to support this approach, ensuring they carry as much weight as any other European degree. It’s accredited by both the European Qualification Framework (EQF) and the Malta Qualification Framework (MQF), with all of its courses having full legal value throughout Europe.
It’s also here where we see OPIT’s approach to practicality come into play via its course structuring.
Take its Bachelor’s degree in computer science as an example.
Yes, that course starts with a focus on theoretical and foundational knowledge. Building a computer and understanding how the device processes instructions is vital information from a programming perspective. But once those foundations are in place, OPIT delivers on its promises of covering the most current topics in the field.
Machine learning, cloud computing, data science, artificial intelligence, and cybersecurity – all valuable to employers – are taught at the undergraduate level. Students benefit from a broader approach to computer science than most institutions are capable of, rather than bogging them down in theory that serves little practical purpose.
Reason 5 – The Learning Experience
Let’s wrap up by honing in on what it’s actually like for students to learn with OPIT.
After all, as Ocleppo points out, one of the main challenges with online education is that students rarely have defined checkpoints to follow. They can start feeling lost in the process, confronted with a metaphorical ocean of information they need to learn, all in service of one big exam at the end.
Alternatively, some students may feel the temptation to not work through the materials thoroughly, focusing instead on passing a final exam. The result is that those students may pass, but they do so without a full grasp of what they’ve learned – a nightmare for employers who already have skill gaps to handle.
OPIT confronts both challenges by focusing on a continuous learning methodology. Assessments – primarily practical – take place throughout the course, serving as much-needed checkpoints for evaluating progress. When combined with the previously mentioned support that OPIT offers, this approach has led to courses that are created from scratch in service of the student’s actual needs.
Choose OPIT for Your Computer Science Education
At OPIT, the focus lies as much on helping students to achieve their dream careers as it does on teaching them. All courses are built collaboratively. With a dedicated faculty combined with major industry players, such as Google and Microsoft, it delivers materials that bridge the skill gap seen in the computer science field today.
There’s also more to come.
Beyond the three degrees OPIT offers, the institution plans to add more. Game development, data science, and cloud computing, to name a few, will receive dedicated degrees in the coming months, accentuating OPIT’s dedication to adapting to the continuous evolution of the computer science industry. Discover OPIT today – your journey into computing starts with the best online education institution available.