Data Mining Articles - Page 18 of 36

The following are some characteristics of data that can strongly affect cluster analysis which is as follows −High Dimensionality − In high-dimensional data sets, the traditional Euclidean concept of density, which is the several points per unit volume, becomes significant. It is considered that as the multiple dimensions increase, the volume increases growingly, and unless the multiple points grow exponentially with the multiple dimensions, the density tends to 0.It can also proximity influence to become more uniform in high-dimensional areas. There is another method to consider this fact is that there are more dimensions (attributes) that contribute to the proximity ... Read More

K-MeansK-means clustering is the partitioning algorithm. K-means recreates each data in the dataset to only one of the new clusters formed. A data or data point is assigned to the adjacent cluster using a measure of distance or similarity.In k-means, an object is generated to the nearest center. It can define cannot-link constraints, and it modifies the center assignment process in k-means to the closest applicable center assignment.When the objects are created to centers in sequence, at each step it can provide the assignments so far do not disorganize some cannot-link constraints. An object is created to the closest center ... Read More

The bisecting K-means algorithm is a simple development of the basic K-means algorithm that depends on a simple concept such as to acquire K clusters, split the set of some points into two clusters, choose one of these clusters to split, etc., until K clusters have been produced.The k-means algorithm produces the input parameter, k, and division a set of n objects into k clusters so that the resulting intracluster similarity is high but the intercluster analogy is low. Cluster similarity is evaluated concerning the mean value of the objects in a cluster, which can be viewed as the cluster’s ... Read More

There are various issues of the K-Means Algorithm which are as follows −Handling Empty Clusters − The first issue with the basic K-means algorithm given prior is that null clusters can be acquired if no points are allocated to a cluster during the assignment phase. If this occurs, then a method is needed to choose a replacement centroid, because the squared error will be larger than necessary.One method is to select the point that is farthest away from some recent centroid. If this removes the point that currently contributes some total squared error. Another method is to select the replacement ... Read More

There are various types of clustering which are as follows −Hierarchical vs Partitional − The perception between several types of clusterings is whether the set of clusters is nested or unnested, or in popular terminology, hierarchical or partitional. A partitional clustering is a distribution of the group of data objects into non-overlapping subsets (clusters) including every data object is in truly one subset.It can allow clusters to have subclusters, therefore it is required hierarchical clustering, which is a group of nested clusters that are assigned as a tree. Every node (cluster) in the tree (except for the leaf nodes) is ... Read More

The process of combining a set of physical or abstract objects into classes of the same objects is known as clustering. A cluster is a set of data objects that are the same as one another within the same cluster and are disparate from the objects in other clusters. A cluster of data objects can be considered collectively as one group in several applications. Cluster analysis is an essential human activity.Cluster analysis is used to form groups or clusters of the same records depending on various measures made on these records. The key design is to define the clusters in ... Read More

There are two approaches for determining the expected support of a pattern using (a concept hierarchy and a neighborhood-based approach called indirect association.Support Expectation Based on Concept HierarchyObjective measures alone cannot be adequate to remove uninteresting infrequent patterns. For instance, consider bread and laptop computer are frequent items. Even though the itemset {bread, Iaptop conputer} is infrequent and possibly negatively correlated, it is not fascinating because their lack of support appears clear to domain experts. Hence, a subjective approach for deciding expected support is required to prevent generating such infrequent patterns.Support Expectation Based on Indirect AssociationConsider a pair of items, ... Read More

The first class of techniques produced for mining infrequent patterns considers each item as a symmetric binary variable. The transaction information can be binarized by augmenting it with negative items. It displays an instance of changing the initial data into transactions having both positive and negative items. By using current frequent itemset generation algorithms including Apriori on the augmented transactions, some negative itemsets can be derived.Such an approach is possible only if several variables are considered as symmetric binary (i.e., it is viewed for negative patterns containing the negation of only a small number of items). If each item should ... Read More

A standard method for handling the graph isomorphism issues is to map each graph into a specific string representation called its code or canonical label. A canonical label has the property that if two graphs are isomorphic, therefore their codes should be equal.This property enables us to test for graph isomorphism by analyzing the canonical labels of the graphs. The first phase toward building the canonical label of a graph is to discover an adjacency matrix description for the graph. It shows an instance of such a matrix for the given graph.A graph can have higher than one adjacency matrix ... Read More

Association analysis algorithms have the probable to make a huge number of patterns. For instance, although the data set include only six items, it can create up to thousands of association rules at specific support and confidence thresholds. As the size and dimensionality of real monetary databases can be large, they can easily end up with thousands or even millions of patterns, some of which cannot be interesting.It is analytical through the patterns to recognize the most interesting ones is not a trivial service because one person's trash can be another person's treasure. It is essential to create a set ... Read More