Rajia cluster analysis
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Cluster analysis is a statistical technique used to group similar objects into clusters. It can be used to reduce the complexity of large datasets by organizing data into a smaller number of homogeneous groups. The key advantages are that it provides a way to classify unclassified data with accuracy while saving resources like time and money. There are different types of cluster analysis methods like hierarchical, centroid-based, distribution-based, and density-based clustering that organize objects into clusters in different ways. Cluster analysis has various applications in fields such as marketing, land use planning, insurance, and earthquake studies.
Rajia cluster analysis
- 1. REHANA RAJ DFK1307 DEPT OF FISH PROCESSING TECHNOLOGY COLLEGE OF FISHERIES MANGALORE CLUSTER ANALYSIS
- 2. Cluster Analysis is a multivariate statistical techniques in which large data set is segregated into several groups based on homogeneity or similarity measures Cluster Analysis make sensible and informative classification of an initially unclassified set of data with desired accuracy, using the variable values observed on each individual It saves lot of resource in terms of time, money etc
- 3. Before clustering After clustering
- 4. To assign observations to groups (‘clusters’) To divide the observations into homogenous and distinct groups To reduce the complexity of data
- 5. Generates several groups of data set which are similar Homogeneous within the group and as much as possible heterogeneous to other groups Normally, data consists of objects or persons Segregation is done based on more than two variables.
- 6. Hierarchical Clustering Centroid-based clustering Distribution-based clustering Density-based clustering
- 7. Hierarchical clustering is a method of cluster analysis which seeks to build a hierarchy of clusters. Two types: Agglomerative (bottom-top): ◦ Start with each document being a single cluster. ◦ Eventually all documents belong to the same cluster. Divisive (top-bottom): ◦ Start with all documents belong to the same cluster. ◦ Eventually each node forms a cluster on its own. No. of clusters need not be k.
- 8. Construction of a tree-based hierarchical diagram usually called dendrogram. E.g., In case of taxonomy classification animal vertebrate fish reptile amphib. mammal worm insect crustacean invertebrate
- 9. In this clustering, clusters are represented by a central vector, which may not necessarily be a member of the data set. Aims to partition on observations into k clusters. Each observation belongs to the cluster with the nearest mean. Here, the no. of clusters is fixed to k(k-means clustering)
- 10. Clusters can be defined as objects belonging to same distribution. It provides correlation and dependence of attributes.
- 11. Clusters are based on density. Objects in these sparse areas - that are required to separate clusters - are usually considered to be noise and border points. The most popular density based clustering method is DBSCAN (density-based spatial clustering of applications with noise). OPTICS (Ordering Points To Identify the Clustering Structure) is a generalization of DBSCAN that handles different densities much better way.
- 12. Density-based clustering with DBSCAN. DBSCAN assumes clusters of similar density, and may have problems separating nearby clusters OPTICS is a DBSCAN variant that handles different densities much better
- 13. 1. Forming the clusters from the given data set – resulting in a new variable that identifies cluster members among the cases (one phase cluster) 2. Description of clusters by re-crossing with the data (Two phase cluster)
- 14. FISH CUTLET FISH FINGER FISH BURGER VALUE ADDED PRODUCTS One phase cluster Forming of clusters by the chosen data set
- 15. FISH CUTLET Seer fish Mackerel Baked Fried Two phase cluster Third phase cluster
- 16. Cuts down the cost of preparing a sampling frame and other administrative factors. No special scales of measurement necessary Visual graphic provides clear understanding of the clusters. Disadvantages: Choice of cluster-forming variables often not based on theory but at random In some cases, determination of clusters is difficult to decide. Advantages :
- 17. Marketing: Help marketers to discover distinct groups in their customer bases, and then use this knowledge to develop targeted marketing programs Land use: Identification of areas of similar land use in an earth observation database Insurance: Identifying groups of motor insurance policy holders with a high average claim cost City-planning: Identifying groups of houses according to their house type, value, and geographical location Earth-quake studies: Observed earth quake epicenters should be clustered along continent faults
- 18. for your kind attention!