A Study of Network Intrusion Detection by Applying
... Where E is the sum of the square error for all objects in the data set; p is the point in space representing given object; and mi is the mean of cluster Ci(p and mi are multidimensional)[21] 2. K-MEDOIDS K-Medoids attempts to minimize the distance between points and its centroid. This clustering alg ...
... Where E is the sum of the square error for all objects in the data set; p is the point in space representing given object; and mi is the mean of cluster Ci(p and mi are multidimensional)[21] 2. K-MEDOIDS K-Medoids attempts to minimize the distance between points and its centroid. This clustering alg ...
Data Mining: Concepts and Techniques — Slides for Textbook
... Clustering Definition • Given a set of data points, each having a set of attributes, and a similarity measure among them, find clusters such that – Data points in one cluster are more similar to one ...
... Clustering Definition • Given a set of data points, each having a set of attributes, and a similarity measure among them, find clusters such that – Data points in one cluster are more similar to one ...
Graph Degree Linkage: Agglomerative Clustering on a
... the cluster and average outdegree to the cluster. Intuitively, if a vertex belongs to a cluster, it should be strongly connected to the cluster, i.e., both its indegree and outdegree are large. Otherwise, either the indegree or outdegree is small. Therefore, the product of indegree and outdegree can ...
... the cluster and average outdegree to the cluster. Intuitively, if a vertex belongs to a cluster, it should be strongly connected to the cluster, i.e., both its indegree and outdegree are large. Otherwise, either the indegree or outdegree is small. Therefore, the product of indegree and outdegree can ...
Data Mining : A Tool for Banking Industry
... the available data to build a model that describes one particular variable of interest in terms of the rest of the available data. For example, analyzing bankruptcy, the target variable is a binary variable that describes if a client was declared on bankruptcy or not. In directed data mining, we try ...
... the available data to build a model that describes one particular variable of interest in terms of the rest of the available data. For example, analyzing bankruptcy, the target variable is a binary variable that describes if a client was declared on bankruptcy or not. In directed data mining, we try ...
Measuring Information Quality for Privacy Preserving Data
... For example in Fig. 1, generalizing the 50 records in F to C (which collectively describes F, G and H) would result in the value of 50 records (for one attribute) being indistinguishable from 2 other values (nodes). With 5 leaves in the taxonomy tree, LM 2 5 for those 50 records in regards to the ...
... For example in Fig. 1, generalizing the 50 records in F to C (which collectively describes F, G and H) would result in the value of 50 records (for one attribute) being indistinguishable from 2 other values (nodes). With 5 leaves in the taxonomy tree, LM 2 5 for those 50 records in regards to the ...
Performance Analysis of Classification Algorithms on Medical
... values to be noted as not applicable. Further, C5.0 provides facilities for defining new attributes as functions of other attributes. Some recent data mining applications are characterized by very high dimensionality, with hundreds or even thousands of attributes. C5.0 can automatically winnow the a ...
... values to be noted as not applicable. Further, C5.0 provides facilities for defining new attributes as functions of other attributes. Some recent data mining applications are characterized by very high dimensionality, with hundreds or even thousands of attributes. C5.0 can automatically winnow the a ...
Knowledge discovery from database Using an integration of
... performing clustering and classification algorithms. The dataset used in this paper is Fisher‟s Iris dataset, consists of 50 samples from each of three species of Iris flowers (Iris setosa, Iris virginica and Iris versicolor). Four features were measured from each sample; they are the length and the ...
... performing clustering and classification algorithms. The dataset used in this paper is Fisher‟s Iris dataset, consists of 50 samples from each of three species of Iris flowers (Iris setosa, Iris virginica and Iris versicolor). Four features were measured from each sample; they are the length and the ...
Web Data Mining Based Business Intelligence and Its
... – or when referring to actual methods, artificial intelligence and machine learning – are more appropriate. The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns such as groups of data records (cluster a ...
... – or when referring to actual methods, artificial intelligence and machine learning – are more appropriate. The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns such as groups of data records (cluster a ...
CHAPTER-25 Mining Multimedia Databases
... data cube. A multimedia data cube can have many dimensions. The following are some examples: the size of the image or video in bytes; the width and height of the frames (or picture), constituting two dimensions; the date on which the image or video was created (or last modified); the format type of ...
... data cube. A multimedia data cube can have many dimensions. The following are some examples: the size of the image or video in bytes; the width and height of the frames (or picture), constituting two dimensions; the date on which the image or video was created (or last modified); the format type of ...
A Web-based Environment for Analysis and Visualization of Spatio
... These tools should be intuitive so that little time is spent obtaining relevant conclusions from the analysis process, such as information on predictions, recurrence patterns, and clustering. Visualization techniques are well known for improving the decision support process [1], once they take advan ...
... These tools should be intuitive so that little time is spent obtaining relevant conclusions from the analysis process, such as information on predictions, recurrence patterns, and clustering. Visualization techniques are well known for improving the decision support process [1], once they take advan ...
Lecture notes for chapter 3 (Powerpoint Slideshow file)
... An index tree hierarchically divides a data set into partitions by value range of some attributes Each partition can be considered as a bucket Thus an index tree with aggregates stored at each node is a hierarchical histogram. Copyright by Jiawei Han, modified ...
... An index tree hierarchically divides a data set into partitions by value range of some attributes Each partition can be considered as a bucket Thus an index tree with aggregates stored at each node is a hierarchical histogram. Copyright by Jiawei Han, modified ...
A Privacy Preserving Algorithm that Maintains Association Rules
... those techniques for some reasons: the attribute level generalization has the disadvantage of creating a lot of data distortion. Many replacements of old values of an attribute with new ones will contribute to direct impact on making wrong in many association rule sets. The cell level generalizatio ...
... those techniques for some reasons: the attribute level generalization has the disadvantage of creating a lot of data distortion. Many replacements of old values of an attribute with new ones will contribute to direct impact on making wrong in many association rule sets. The cell level generalizatio ...
Association Rule Mining in Peer-to-Peer Systems
... is final and accurate. At each point in time, new information can arrive from a far-away branch of the system and overturn the node’s picture of the correct result. The best that can be done in these circumstances is for each node to maintain an assumption of the correct result and update it wheneve ...
... is final and accurate. At each point in time, new information can arrive from a far-away branch of the system and overturn the node’s picture of the correct result. The best that can be done in these circumstances is for each node to maintain an assumption of the correct result and update it wheneve ...
Indexing and Data Access Methods for Database Mining
... looked at the language support. DMQL [6] is a mining query language designed to support the wide specMost of today’s techniques for data mining and associa- trum of common mining tasks. It consists of specification rule mining (ARM) in particular, can be aptly termed tions of four main primitives, w ...
... looked at the language support. DMQL [6] is a mining query language designed to support the wide specMost of today’s techniques for data mining and associa- trum of common mining tasks. It consists of specification rule mining (ARM) in particular, can be aptly termed tions of four main primitives, w ...
Association Rule Pattern Mining Approaches Network
... monitored traffic from the normal profile is measured. Various different implementations of this technique have been proposed, based on the metrics used for measuring traffic profile deviation. Misuse technique looks for patterns and signatures of already known attacks in the network traffic. A cons ...
... monitored traffic from the normal profile is measured. Various different implementations of this technique have been proposed, based on the metrics used for measuring traffic profile deviation. Misuse technique looks for patterns and signatures of already known attacks in the network traffic. A cons ...
Big Data Analytics for Dynamic Energy Management in Smart Grids
... interconnected processors, which can be used to estimate approximate functions that depend on a large number of inputs when there is not an accurate mathematical model to describe the phenomenon [45]. This can be achieved by weighting and transforming the input values by a suitable function with the ...
... interconnected processors, which can be used to estimate approximate functions that depend on a large number of inputs when there is not an accurate mathematical model to describe the phenomenon [45]. This can be achieved by weighting and transforming the input values by a suitable function with the ...
Nonlinear dimensionality reduction
High-dimensional data, meaning data that requires more than two or three dimensions to represent, can be difficult to interpret. One approach to simplification is to assume that the data of interest lie on an embedded non-linear manifold within the higher-dimensional space. If the manifold is of low enough dimension, the data can be visualised in the low-dimensional space.Below is a summary of some of the important algorithms from the history of manifold learning and nonlinear dimensionality reduction (NLDR). Many of these non-linear dimensionality reduction methods are related to the linear methods listed below. Non-linear methods can be broadly classified into two groups: those that provide a mapping (either from the high-dimensional space to the low-dimensional embedding or vice versa), and those that just give a visualisation. In the context of machine learning, mapping methods may be viewed as a preliminary feature extraction step, after which pattern recognition algorithms are applied. Typically those that just give a visualisation are based on proximity data – that is, distance measurements.