Graph Clustering Methods in Data Mining

Technological advancement has made data analysis and visualization easy. These include the development of software and hardware technologies. According to Big Data, 90% of global data has doubled after 1.2 years since 2014. In every decade we live, we can attest that data analysis is becoming more straightforward and quick.  This shows that the data analysis and visualization industry needs more people. Surprisingly, data mining has a shortage of skilled personnel. This blog benefits you because we will learn more about Graph Clustering Methods in Data Mining.  By the end of it all, we will have an understanding of the graph clustering methods and their applications in our actual lives. But first, we need to understand data mining and graph clustering. Let us proceed to the next section.

Graph Clustering:

Data mining involves analyzing large data sets, which helps you to identify essential rules and patterns in your data story. On the other hand, graph clustering is classifying similar objects in different clusters on one graph.  In a biological instance, the objects can have similar physiological features, such as body height. Still, the objects can be of the same species. When you want to perform graph clustering, some parameters you can consider include data point density and the distance between data points. If you are a data scientist or a retailer, you have a significant role in graph clustering. This is because it can help you gather vital information on how data points relate to each other. When you use graph clustering methods in data mining, you identify relationships in your data story.

Applications of Graph Clustering Methods in Data Mining:

Let us take a look at some of these applications, which include:

• In the Business World: 
  • You can use graph clustering methods to group your customers as a marketer. 
  • You can group your customers based on their purchasing behavior and preferences when you obtain meaningful insights.
  • You can also classify your products and the geographical location where they sell the most.
  • As a business person, you can use graph clustering to help you identify how various social media platforms affect your business model.
• In Biology:
  • If you are a biology student or scientist, you can use graph clustering methods in classifying plants and animals.
    In your biology classes, one of the basics was classifying plant taxonomies based on their genes. Graph clustering methods are handy as they can help you know various species and what they share in common.
• In Geography:
  • Graph Clustering Methods in Data Mining can help you as a geography expert. You can establish insights such as forest coverage and population distribution.
  • You can classify which areas experience similar climatic conditions. Still, you can group particular geographical regions based on their rainfall distribution patterns.
  • The practical examples are just the tip of the iceberg for utilizing graph clustering methods. We hope you are following closely so far.
    Let us proceed to the crucial part of this blog. We will look at some of the Graph Clustering Methods in Data Mining.

Hierarchical Graph Clustering:

It is one of the most common graph clustering methods you can use. When you utilize this clustering method, your graph appears as partitions of hierarchical structures. This method has two types of strategies, namely:

• Divisive strategy
• Agglomerative strategy
  

When drawing your graph in the divisive strategy, you group your data points in one cluster at the start. As you move down the hierarchy, the cluster splits the significant data points at each step. On the other hand, when using the agglomerative strategy, you start from the bottom to the top. Every node in your graph represents a different cluster. Every cluster pairs as you move up until all the nodes belong to one cluster. One of the advantages of hierarchical graph clustering methods in data mining is that it is simple to implement. Classifying various categories with this method gives the best results.

Let us take a look at another method.

K-Means Graph Clustering Method:

When you use this method, you partition your graph (line chart, bar chart, gauge chart, etc.) into clusters that appear with a k-shape. You can use k-means to compute the centroids and their vector quantities. It takes you easy steps in constructing the K-mean clustering method, which includes:

1. You can select K to represent the original centroids.
2. Next is to assign points to the nearest centroids from your K-cluster.
3. Conduct a re-evaluation of your centroids to ensure they do not change.
4. One advantage you can obtain from this method is that it can help you process large data sets. 
   

Density-Based Graph Clustering Method:

Density-based methods work wonders when you want to identify clusters in larger data sets. This is because you can analyze data points based on their density. Two close data points are termed, neighbors. The concept behind this method is density connectivity and density reachability. You will find many data scientists and business persons using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Some of the steps you can use in this method include:

• You can begin the clustering process when you find enough data points in your graph. 
• Your current data point acts as the starting point.
•  Your starting point to the new cluster uses a similar distance which defines its neighborhood. 
•  You will continue this process in every new cluster until you label all data points.
• An advantage you can find from this graph clustering method is that you can recognize noisy data.

As we have learned from the various clustering methods, you can group data into sets. This task is essential for understanding the relationship among your data groups. However, Graph Clustering Methods in Data Mining have their various disadvantages. It is up to you to choose the suitable graph clustering method for your data.