What Does Clustering in Data Mining Mean?

Data mining and clustering are closely interlinked. They both focus on the pattern recognition underlying a particular dataset.

Mainly, it’s a joint effort of machine learning, pattern recognition and statistics. They help in discovering patterns in data. Clustering is one of the various methods of data mining.

What is clustering in data mining?

Generally, the mining of data ends up at spotting the pattern. If you talk about clustering in particular, it’s an unsupervised data mining method that splits the data into natural groups. In other words, clustering is the statistical distribution of data into subclasses. Each subclass showcases a group of similar objects.  It’s a kind of unsupervised algorithm.

Let’s consider this example to clarify its meaning. When you type a phrase in Google, it immediately monitors. Whenever you browse it again, it lines up an array of ads that are motivated by your previous search. Its bots take a few minutes to scan what you explored. Likewise, many other users would have browsed the similar or related information. But, their phrases might differ. Its bots put billions of searches in algorithms to make a list of the most searchable phrases. It’s what the data mining is.

The unsupervised algorithms use multiple variables describing the data as an input. Unlike supervised algorithm, it carries no variable to predict.

What are its various methods?

The data can have various types, like survey, report, table, images and so on. Its various methods deal with the type of data in cluster analysis for data mining. Thereby, the outcome appears in a decisive role.

1. Partitioning method: Let’s say a dataset comprises n objects whereas they partition into group k. It implies that each k group will have n objects provided that:

• Each cluster should have at least one object.
• One object should belong to one cluster only.

This kind of clustering in data mining is effective initially. It should be followed by the iterative relocation technique for refined clustering.

1. Hierarchical method: It’s a hierarchical decomposition of the data objects. Its agglomerative approach begins with clustering each object in a group. Subsequently, the closely related objects are merged until one object is left as per the bottom-up approach.

On the other hand, the miner can employ a top-down approach, viz. divisive approach. It begins with clustering all objects into one group. Then, it is split up into smaller clusters.

1. Density-based method: As its name suggests, this method widens the radius of a cluster as long as the density of the neighbouring cluster exceeds its threshold.
2. Model-based method: It is based on hypothetical modeling. A hypothetical model is propped up to find the best fit of the data. The density function is kept at the core. Then, the spatial distribution of data appears.  While taking into account the outlier or noise, the standard statistics determine clusters.
3. Constraint-based method: This method reflects the incorporation of users or an application-oriented constraint like user’s expectation into clusters.

What are its applications?

1. Market research: Market research requires deep insight into the comparative and predictive analysis. This clustering broadly helps in recognizing hidden patterns, analysis and strategy formation.
2. Internet algorithm: The World Wide Web uses it for comprehending searches to filter accurate results or information.
3. Pattern recognition: Many banks use outlier detection application for screening fraud patterns via credit card.
4. Image processing: Let’s say, the government wants to get the exact idea about land acquisition on a particular location. Clustering helps in determining groups of houses according to their type, value and geographic location appearing in the images.
5. Data mining: It helps in classifying marketing loops, customer analysis, deriving plant and animal taxonomies, gene categorization and insight about any targeted domain.