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Clustering paradigms and Partitioning Algorithms
The document discusses clustering as a method of grouping similar objects into clusters while separating dissimilar ones. It highlights various applications of cluster analysis, including market research, biology, urban planning, and fraud detection, emphasizing its importance in data analysis. The authors are Umang Mishra and Navdeep Rawat, from Punjab University.
Clustering paradigms and Partitioning Algorithms
- 1. Clustering Paradigms & Partitioning Algorithms Submitted To:- Prof. Neeru Mago Submitted By:- Name - Umang Mishra & Navdeep Rawat Roll no - 1631 College – Panjab Univsersity (P.U.S.S.G.R.C)
- 3. What is Clustering Clusteris a group of objects that belongs to the same class. In other words, similar objects are grouped in one cluster and dissimilar objects are grouped in another cluster.
- 4. Applications of ClusterAnalysis Clustering analysis is broadly used in many applications such as market research, pattern recognition, data analysis, and image processing. Clustering can also help marketers discover distinct groups in their customer base. And they can characterize their customer groups based on the purchasing patterns. In the field of biology, it can be used to derive plant and animal taxonomies, categorize genes with similar functionalities and gain insight into structures inherent to populations. Clustering also helps in identification of areas of similar land use in an earth observation database. It also helps in the identification of groups of houses in a city according to house type, value, and geographic location. Clustering also helps in classifying documents on the web for information discovery. Clustering is also used in outlier detection applications such as detection of credit card fraud. As a data mining function, cluster analysis serves as a tool to gain insight into the distribution of data to observe characteristics of each cluster.
- 62. Umang Mishra &Navdeep Rawat Roll no -1631 College – Panjab University(P.U.S.S.G.R.C)