What Does Clustering in Data Mining Mean?

Posted by aQb Solutions on July 23rd, 2019

What is Clustering in Data Mining?

Clustering in Data Mining may be defined as the logical grouping of a particular set of objects based on their characteristics, and aggregating them according to their similar qualities.

Clustering in Data Mining is quite similar to sampling in Economics. It helps in identifying and thereafter grouping similar objects in one cluster and dissimilar objects in another cluster.

Clustering in Data Mining also helps in classifying documents on the web for information discovery.

How is data clustered in data mining?

• A cluster of similar data objects is usually treated as one group
• Data is partitioned into groups based on data similarity
• Relevant labels or tags are assigned to the respective groups

Different Clustering Techniques

What are the different clustering techniques applied in data mining?

In this discussion, we have discussed the six most popular clustering algorithms applied in data mining.

1. Clustering Algorithm for Data Mining

A clustering algorithm is used in data mining to classify each data point into a specific group. While data points in the same group have similar properties, data points in different groups are quite likely to have highly dissimilar properties or features. Clustering is a common technique for statistical data analysis used in many fields.

2. K-means clustering:

K-means clustering may be explained as a machine learning clustering technique that is used to simplify large datasets into smaller and simple datasets. K-means clustering usually generates a specific number of disjointed and non-hierarchical data clusters. This technique works well for generating globular clusters.

Clear patterns are evaluated and similar data sets are clubbed together. The variable K represents the number of groups in the data.

3. Mean-Shift Clustering:

Mean shift clustering may be described as a sliding-window-based algorithm. This algorithm is known for locating dense areas of data points. The centroid-based algorithm aims at finding the center points of each group of data. After this, the candidate windows are filtered in a post-processing stage to sort out the near-duplicates, forming the final set of center points and their corresponding groups.

4. Density-Based Spatial Clustering of Applications with Noise (DBSCAN):

DBSCAN, another density-based clustered algorithm, is quite similar to the mean-shift algorithm. It begins with an arbitrary starting data point that is yet to be discovered or visited. The primary advantage of working with DBSCAN is that does it not require a pre-set number of clusters. It is capable of identifying outliers as noises, unlike mean-shift which treats all into a cluster. DBSCAN can also find arbitrarily sized and arbitrarily shaped clusters. However, it cannot perform well when the clusters are of varying density.

5. Expectation–Maximization (EM) Clustering using Gaussian Mixture Models (GMM):

Gaussian Mixture Models (GMMs) are better than K-Means, giving us more flexibility. A Gaussian mixture model (GMM) is useful for modeling data that is present in several groups. The groups might be different from each other, but data points in the same group can be well-modeled by a Gaussian distribution. In a GMM model, the primary motive is to maximize the likelihood function with respect to the parameters comprising the means and covariances of the components and the mixing coefficients.

For example, say the price of a softback exercise book is normally distributed with mean .00 and standard deviation .00. Similarly, the price of a hardback cover exercise book is normally distributed with mean and variance .50.

Question: Is the price of a randomly chosen exercise book normally distributed?

The answer is no. We can understand this by simply looking at the fundamental property of the normal distribution: it is usually highest near the center and quickly drops off as you go further. But, the distribution of a randomly chosen exercise book is bimodal: the center of the distribution is near , but the probability of finding an exercise book near that price is lower than the probability of finding an exercise book for a few dollars more or a few dollars less than the probability of finding an exercise book for a few dollars more or a few dollars less.

6. Agglomerative Hierarchical Clustering:

Hierarchical clustering algorithms usually fall into two main categories: top-down or bottom-up. The Bottom-up algorithms treat each data point as a single cluster at the outset and then successively merge (or agglomerate, technically speaking) pairs of clusters until all clusters have been merged into a single cluster that contains all data points. Bottom-up hierarchical clustering is also called hierarchical agglomerative clustering or HAC. This hierarchy of clusters is represented as a tree. The root of the tree represents the unique cluster that gathers all the samples, while the leaves are the clusters with only one sample.

Looking for more insights on Data Mining techniques and Big Data Analytics? Log on to https://www.aqbsolutions.com/