CSE5334 Data Mining
... Memorizes entire training data and performs classification only if attributes of record match one of the training examples exactly ...
... Memorizes entire training data and performs classification only if attributes of record match one of the training examples exactly ...
Different Data Mining Techniques And Clustering Algorithms
... Data mining techniques are basically categorised into two major groups as Supervised learning and Unsupervised learning. Clustering is a process of grouping the similar data sets into groups. These groups should have two properties like dissimilarity between the groups and similarity within the grou ...
... Data mining techniques are basically categorised into two major groups as Supervised learning and Unsupervised learning. Clustering is a process of grouping the similar data sets into groups. These groups should have two properties like dissimilarity between the groups and similarity within the grou ...
An adaptive rough fuzzy single pass algorithm for clustering large
... data. Hence the methods to handle them must be e/cient both in terms of the number of data set scans and memory usage. Several algorithms have been proposed in the literature for clustering large data sets viz; CLARANS [1], DB-SCAN [1], CURE [1], K-Means [2], etc. Most of these require more than one ...
... data. Hence the methods to handle them must be e/cient both in terms of the number of data set scans and memory usage. Several algorithms have been proposed in the literature for clustering large data sets viz; CLARANS [1], DB-SCAN [1], CURE [1], K-Means [2], etc. Most of these require more than one ...
An Approach to Text Mining using Information Extraction
... clustering that is based on links in order to measure the similarity between a pair of data points. Clustering points based on only the closeness or similarity between them is not strong enough to distinguish two “not well-separated” clusters because it is possible for points in different clusters t ...
... clustering that is based on links in order to measure the similarity between a pair of data points. Clustering points based on only the closeness or similarity between them is not strong enough to distinguish two “not well-separated” clusters because it is possible for points in different clusters t ...
Review Paper on Clustering and Validation Techniques
... clustering algorithm applied to same dataset produce different results. Even the same algorithm, with the different values of parameter produces different clusters. Therefore it becomes necessary to validate or evaluate the result produce by the clustering method. The evaluation criteria are categor ...
... clustering algorithm applied to same dataset produce different results. Even the same algorithm, with the different values of parameter produces different clusters. Therefore it becomes necessary to validate or evaluate the result produce by the clustering method. The evaluation criteria are categor ...
Nearest-neighbor chain algorithm
In the theory of cluster analysis, the nearest-neighbor chain algorithm is a method that can be used to perform several types of agglomerative hierarchical clustering, using an amount of memory that is linear in the number of points to be clustered and an amount of time linear in the number of distinct distances between pairs of points. The main idea of the algorithm is to find pairs of clusters to merge by following paths in the nearest neighbor graph of the clusters until the paths terminate in pairs of mutual nearest neighbors. The algorithm was developed and implemented in 1982 by J. P. Benzécri and J. Juan, based on earlier methods that constructed hierarchical clusterings using mutual nearest neighbor pairs without taking advantage of nearest neighbor chains.