Clustering Methods

Introduction to Clustering Methods

This article presents an overview of different clustering methods used in data mining techniques with different principles. Clustering is a set of data objects organized into a different logical grouping. Grouping similar data items and assigning similar data items into individual clusters. Clustering is performed in large data sets for unsupervised learning. During this, we perform partition on the set of data into groups. The structure of clustering is represented as follows with subsets. C= c1, c2…c_n. As clustering groups have similar objects some measures have to be taken in clustering methods to determine distance and similarity measures. Clustering methods are based on probabilistic models. Data mining requires clustering for the scalability to deal with high databases, handling multi-dimensional space, to deal with erroneous data and noise.

Explain Clustering Methods?

This clustering method helps in grouping valuable data into clusters and from that picks appropriate results based on different techniques. Example, in information retrieval the results of the query are grouped into small clusters, and each cluster has irrelevant results. By Clustering techniques, they are grouped into similar categories and each category is subdivided into sub-categories to assist in the exploration of queries output. There are various types of clustering methods, they are

• Hierarchical methods
• Partitioning methods
• Density-based
• Model-based clustering
• Grid-based model

Following are an overview of techniques used in data mining and artificial intelligence.

1. Hierarchical Method

This method creates a cluster by partitioning in an either top-down and bottom-up manner. Both these approach produces dendrogram they make connectivity between them. The dendrogram is a tree-like format that keeps the sequence of merged clusters. Hierarchical methods are produced multiple partitions with respect to similarity levels. They are divided into Agglomerative hierarchical clustering and divisive hierarchical clustering. Here a cluster tree is created by using merging techniques. For splitting process divisive is used, merging uses agglomerative. Agglomerative clustering involves :

1. Initially taking all the data points and considering them as individual clusters start from top-down manner. These clusters are merged until we obtained the desired results.
2. The next two similar clusters are grouped together to form a huge single cluster.
3. Again calculating proximity in the huge cluster and merge the similar clusters.
4. The final step involves merging all the yielded clusters at each step to form a final single cluster.

2. Partitioning Method:

The main goal of partition is relocation. They relocate partitions by shifting from one cluster to another which makes an initial partitioning. It divides ‘n’ data objects into ‘k’ number of clusters. This partitional method is preferred more than a hierarchical model in pattern recognition. The following criteria are set to satisfy the techniques:

• Each cluster should have one object.
• Each data object belongs to a single cluster.

The most commonly used Partition techniques are the K-mean Algorithm. They divide into ‘K’ clusters represented by centroids. Each cluster center is calculated as a mean of that cluster and the R function visualizes the result. This algorithm has the following steps:

1. Selecting K objects randomly from the data set and forms the initial centers (centroids)
2. Next assigning Euclidean distance between the objects and mean center.
3. Assigning a mean value for each individual cluster.
4. Centroid update steps for each ‘k’ Clusters.

3. Density Model:

In this model, clusters are defined by locating regions of higher density in a cluster. The main principle behind them is concentrating on two parameters: max radius of the neighborhood and min number of points. The density-based model identifies clusters of different shapes and noise. It works by detecting patterns by estimating the spatial location and the distance to the neighbor’s method used here is DBSCAN (Density-based spatial clustering) which gives hands for large spatial databases. Using three data points for clustering namely Core points, Border points, and outliers. The primary goal is to identify the clusters and their distribution parameters. The clustering process is stopped with the need for density parameters. To find the clusters it is important to have a parameter Minimum features Per Cluster in calculating core-distance. The three different tools provided by this model are DBSCAN, HDBSCAN, Multi-scale.

4. Model-Based Clustering

This model combines two or three clusters together from the data distribution. The basic idea behind this model is it is necessary to divide data into two groups based on the probability model (Multivariate normal distributions). Here each group is assigned as concepts or class. Each component is defined by a density function. To find the parameter in this model Maximum Likelihood estimation is used for the fitting of the mixture distribution. Each cluster ‘K’ is modelized by Gaussian distribution with two-parameter µ_k mean vector and £_k covariance vector.

5. Grid-Based Model

In this approach, the objects are considered to be a space-driven by partitioning the space into a finite number of cells to form a grid. With the help of the grid, the clustering technique is applied for faster processing which is typically dependent on cells not on objects. Steps involved are:

• Creation of grid structure
• Cell density is calculated for each cell
• Applying a sorting mechanism to their densities.
• Searching cluster centers and traversal on neighbor cells to repeat the process.

Importance of Clustering Methods

1. Having clustering methods helps in restarting local search procedure and remove the inefficiency. Clustering helps to determine the internal structure of the data.
2. This clustering analysis has been used for model analysis, vector region of attraction.
3. Clustering helps in understanding the natural grouping in a dataset. Their purpose is to make sense to partition the data into some group of logical groupings.
4. Clustering quality depends on the methods and to identify hidden patterns.
5. They play a wide role in applications like marketing economic research, weblogs to identify patterns in similarity measures, Image processing, Spatial research.
6. They are used in outlier detections to detect credit card fraudulence.

Conclusion

Clustering is considered to be a general task to solve the problem which formulates optimization problem. It plays key importance in the field of data mining and data analysis. We have seen different clustering methods that divide the data set depends on the requirements. Most of the research is based on traditional techniques like K-means and hierarchical models. Cluster areas are applied in high dimensional states which forms a future scope of researchers.

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