Agglomerative Independent Variable Group Analysis
... presented in Table 1. All the features in all the data sets were continuous and they were normalised to zero mean and unit variance. The AIVGA algorithms were used for feature selection by running them on the training part of the data set consisting of both the features and the target labels and not ...
... presented in Table 1. All the features in all the data sets were continuous and they were normalised to zero mean and unit variance. The AIVGA algorithms were used for feature selection by running them on the training part of the data set consisting of both the features and the target labels and not ...
combined mining approach to generate patterns
... miss some important data that may be filtered out during sampling. If we have to deal with some distinguish large data sets then joining of those data sets into one data set may not be possible as that would be more time and space consuming. More often this approach of handling multiple data sources ...
... miss some important data that may be filtered out during sampling. If we have to deal with some distinguish large data sets then joining of those data sets into one data set may not be possible as that would be more time and space consuming. More often this approach of handling multiple data sources ...
Clustering and Approximate Identification of Frequent Item Sets
... proportion of frequent item sets, particularly in data with low density or high density. Since representing a data set as bit sequences is very space-efficient large amounts of data can be accommodated. The other major idea of the paper is to use a modification of an agglomerative clustering techniq ...
... proportion of frequent item sets, particularly in data with low density or high density. Since representing a data set as bit sequences is very space-efficient large amounts of data can be accommodated. The other major idea of the paper is to use a modification of an agglomerative clustering techniq ...
Critical Issues with Respect to Clustering
... If there are K ‘real’ clusters then the chance of selecting one centroid from each cluster is small. ...
... If there are K ‘real’ clusters then the chance of selecting one centroid from each cluster is small. ...
spatial data mining for finding nearest neighbor and outlier detection
... problems needs more focus on textual data in spatial queries [17]. Ian De Felipe [13] proposed an algorithm using R-tree used to find the objects closer to query location and that contains a set of keywords also. These keywords include the attributes of the objects. In spatial databases, if any user ...
... problems needs more focus on textual data in spatial queries [17]. Ian De Felipe [13] proposed an algorithm using R-tree used to find the objects closer to query location and that contains a set of keywords also. These keywords include the attributes of the objects. In spatial databases, if any user ...
Question Bank
... minimum risk based on their applications. 21 Write short notes on (a) data warehouse (b) multimedia databases (c) Time series data (a) Data warehouse: is a subject oriented, integrated, time variant and non volatile repository used for data mining purposes. (explain briefly) (b) Multimedia databases ...
... minimum risk based on their applications. 21 Write short notes on (a) data warehouse (b) multimedia databases (c) Time series data (a) Data warehouse: is a subject oriented, integrated, time variant and non volatile repository used for data mining purposes. (explain briefly) (b) Multimedia databases ...
A Novel RFE-SVM-based Feature Selection Approach for
... neighborhood of the current solution and replaces it with better neighbor if one exists. In this paper, we devise effective LS procedures inspired from successfully search techniques adapted to the FS. The following paragraphs detail the neighborhood structures that will be deployed within the local ...
... neighborhood of the current solution and replaces it with better neighbor if one exists. In this paper, we devise effective LS procedures inspired from successfully search techniques adapted to the FS. The following paragraphs detail the neighborhood structures that will be deployed within the local ...
Classification Rules and Genetic Algorithm in Data
... Here the Rules are Learned Sequentially, One at a time (for one Class at a time) directly from the Training Data (i.e without having to generate a Decision Tree first) using a Sequential Covering Algorithm. iv. Classification by Backpropagation Backpropagation is the most popular Neural Network Lear ...
... Here the Rules are Learned Sequentially, One at a time (for one Class at a time) directly from the Training Data (i.e without having to generate a Decision Tree first) using a Sequential Covering Algorithm. iv. Classification by Backpropagation Backpropagation is the most popular Neural Network Lear ...
Medical Records Clustering Based on the Text Fetched from Records
... pattern. There are many types of data mining. One of the techniques among them is the clustering technique. Clustering is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups (clusters). It is a main task of e ...
... pattern. There are many types of data mining. One of the techniques among them is the clustering technique. Clustering is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups (clusters). It is a main task of e ...
Clustering Performance on Evolving Data Streams
... subset or even none of the competing solutions, making the assessment of their actual effectiveness tough. Moreover, the majority of experimental evaluations use only small amounts of data. In the context of data streams this is disappointing, because to be truly useful the algorithms need to be cap ...
... subset or even none of the competing solutions, making the assessment of their actual effectiveness tough. Moreover, the majority of experimental evaluations use only small amounts of data. In the context of data streams this is disappointing, because to be truly useful the algorithms need to be cap ...
Using Projections to Visually Cluster High
... concise and interpretable information within that data—is called knowledge discovery in databases (KDD). Data mining refers to one specific step in the KDD process— namely, to the application of algorithms that can extract hidden patterns from data (see the “KDD Process” sidebar for more information ...
... concise and interpretable information within that data—is called knowledge discovery in databases (KDD). Data mining refers to one specific step in the KDD process— namely, to the application of algorithms that can extract hidden patterns from data (see the “KDD Process” sidebar for more information ...
APPLICATION OF ARTIFICIAL INTELLIGENCE BASED
... K-means clustering is a method of cluster analysis whose aim is to partition n observations into k clusters. Every observation belongs to a cluster with the nearest mean [34]. Guan, et al. present a clustering heuristic for intrusion detection called Y-means [35]. This heuristic is based on the K-me ...
... K-means clustering is a method of cluster analysis whose aim is to partition n observations into k clusters. Every observation belongs to a cluster with the nearest mean [34]. Guan, et al. present a clustering heuristic for intrusion detection called Y-means [35]. This heuristic is based on the K-me ...
Hierarchical Clustering
... closer (more similar) to the “center” of a cluster, than to the center of any other cluster – The center of a cluster is often a centroid, the average of all the points in the cluster, or a medoid, the most “representative” point of a cluster ...
... closer (more similar) to the “center” of a cluster, than to the center of any other cluster – The center of a cluster is often a centroid, the average of all the points in the cluster, or a medoid, the most “representative” point of a cluster ...
An accurate MDS-based algorithm for the visualization of large
... 3 Experiments on real datasets We tested our approach on two well known real datasets, namely satimage and abalone from the UCI repository [9] for the following reason: In order to assess the accuracy of the results obtained by our approach, we need to compare their Stress values to the ones obtaine ...
... 3 Experiments on real datasets We tested our approach on two well known real datasets, namely satimage and abalone from the UCI repository [9] for the following reason: In order to assess the accuracy of the results obtained by our approach, we need to compare their Stress values to the ones obtaine ...
The Role of Hubness in Clustering High-Dimensional Data
... There has been previous work on how well highhubness elements cluster, as well as the general impact of hubness on clustering algorithms [23]. A correlation between low-hubness elements (i.e., antihubs or orphans) and outliers was also observed. A low hubness score indicates that a point is on avera ...
... There has been previous work on how well highhubness elements cluster, as well as the general impact of hubness on clustering algorithms [23]. A correlation between low-hubness elements (i.e., antihubs or orphans) and outliers was also observed. A low hubness score indicates that a point is on avera ...
market basket analysis using fp growth and apriori
... This operator calculates all frequent item sets from an example set by building a FP-tree data structure on the transaction data base. This is a very compressed copy of the data which in many cases fits into main memory even for large data bases. All frequent item sets are derived from this FP-tree. ...
... This operator calculates all frequent item sets from an example set by building a FP-tree data structure on the transaction data base. This is a very compressed copy of the data which in many cases fits into main memory even for large data bases. All frequent item sets are derived from this FP-tree. ...
