What are the major requirements of clustering analysis?

What are the major requirements of clustering analysis?

Requirements of Clustering in Data Mining Scalability − We need highly scalable clustering algorithms to deal with large databases. Ability to deal with different kinds of attributes − Algorithms should be capable to be applied on any kind of data such as interval-based (numerical) data, categorical, and binary data.

What is cluster analysis good for?

Clustering (sometimes called cluster analysis) is usually used to classify data into structures that are more easily understood and manipulated.

What are the properties of clusters?

Other significant physical properties of clusters are their electric, magnetic, and optical properties. The electric properties of clusters, such as their conductivity and metallic or insulating character, depend on the substance and the size of the cluster.

What is a good Dunn index?

The Dunn Index has a value between zero and infinity, and should be maximized.

What is good clustering in data mining?

A good clustering method will produce high quality clusters in which: the intra-class (that is, intra intra-cluster) similarity is high. the inter-class similarity is low. The quality of a clustering result also depends on both the similarity measure used by the method and its implementation.

How can cluster analysis be improved?

K-means clustering algorithm can be significantly improved by using a better initialization technique, and by repeating (re-starting) the algorithm. When the data has overlapping clusters, k-means can improve the results of the initialization technique.

What are the characteristic of clustering techniques in data mining?

In clustering, a group of different data objects is classified as similar objects. One group means a cluster of data. Data sets are divided into different groups in the cluster analysis, which is based on the similarity of the data. After the classification of data into various groups, a label is assigned to the group.

What properties a clustering algorithm includes?

Clustering algorithms can have different properties: Hierarchical or flat: hierarchical algorithms induce a hierarchy of clusters of decreasing generality, for flat algorithms, all clusters are the same. Iterative: the algorithm starts with initial set of clusters and improves them by reassigning instances to clusters.

What are the characteristics of good clustering methods?

A: A good clustering method will produce high-quality clusters, which means there is high similarity between observations in a single cluster, and low similarity between observations in different clusters. The quality of the clustering result depends on both the similarity measure used by the method and its implementation.

How do you perform a cluster analysis?

Typically, cluster analysis is performed on a table of raw data, where each row represents an object and the columns represent quantitative characteristic of the objects. These quantitative characteristics are called clustering variables. For example, in the table below there are 18 objects, and there are two clustering variables, x and y.

What is a data cluster?

•  Cluster: a collection of data objects –  Similar to one another within the same cluster –  Dissimilar to the objects in other clusters •  Cluster analysis –  Grouping a set of data objects into clusters •  Clustering is unsupervised classification: no predefined classes •  Typical applications

What is a clustering algorithm?

Clustering algorithms use a distance measure or metric to determine how to separate observations in the different groups. The most common one is called Euclidean distance, which shows how far one center of a cluster is from another center of a cluster, but there are many options.