No Slide Title
... Cluster analysis groups objects based on their similarity and has wide applications Measure of similarity can be computed for various types of data Clustering algorithms can be categorized into partitioning methods, hierarchical methods, density-based methods, grid-based methods, and model-based met ...
... Cluster analysis groups objects based on their similarity and has wide applications Measure of similarity can be computed for various types of data Clustering algorithms can be categorized into partitioning methods, hierarchical methods, density-based methods, grid-based methods, and model-based met ...
Analysis of Game Theoretic Approach in Data Mining Security
... strategy is a rule that tells him which action to choose at each instant of the game, given his information set. A strategy problem is an ordered set consisting of one strategy for each of the players in the game. Equilibrium is a strategy problem consisting of a best strategy for each of the player ...
... strategy is a rule that tells him which action to choose at each instant of the game, given his information set. A strategy problem is an ordered set consisting of one strategy for each of the players in the game. Equilibrium is a strategy problem consisting of a best strategy for each of the player ...
unit-5 - E
... Exhaustive partition of the space of input variables. In this manner, any observation x will be classified by one and only one rule (namely the branch defining the region within which it lies). The set of rules is said to "cover" the input space in this manner. We can see that it may be worth consid ...
... Exhaustive partition of the space of input variables. In this manner, any observation x will be classified by one and only one rule (namely the branch defining the region within which it lies). The set of rules is said to "cover" the input space in this manner. We can see that it may be worth consid ...
Data mining: some basic ideas
... Data mining • Categorization and segmentation: a given population of events or items can be partitioned into sets of “similar” elements: – a population of treatment data may be divided into groups based on similarity of side effects – a population may be categorized into groups from “most likely to ...
... Data mining • Categorization and segmentation: a given population of events or items can be partitioned into sets of “similar” elements: – a population of treatment data may be divided into groups based on similarity of side effects – a population may be categorized into groups from “most likely to ...
CIS 690 (Implementation of High-Performance Data Mining Systems
... – The process of automatically extracting valid, useful, previously unknown, and ultimately comprehensible information from large databases and using it to make crucial business decisions – “Torturing the data until they confess” ...
... – The process of automatically extracting valid, useful, previously unknown, and ultimately comprehensible information from large databases and using it to make crucial business decisions – “Torturing the data until they confess” ...
Data Preparation (Data preprocessing)
... • Linear regression, Multiple linear regression, Nonlinear regression ...
... • Linear regression, Multiple linear regression, Nonlinear regression ...
Slides - Network Protocols Lab
... – A 22 (sub)matrix is a -coherent cluster if its D value is less than or equal to . – An mn matrix X is a -coherent cluster if every 22 submatrix of X is -coherent cluster. • A -coherent cluster is a maximum -coherent cluster if it is not a submatrix of any other -coherent cluster. ...
... – A 22 (sub)matrix is a -coherent cluster if its D value is less than or equal to . – An mn matrix X is a -coherent cluster if every 22 submatrix of X is -coherent cluster. • A -coherent cluster is a maximum -coherent cluster if it is not a submatrix of any other -coherent cluster. ...
COMP 790-090 Data Mining: Concepts
... Automatically identifying subspaces of a high dimensional data space that allow better clustering than original space CLIQUE can be considered as both density-based and grid-based It partitions each dimension into the same number of equal length interval It partitions an m-dimensional data space int ...
... Automatically identifying subspaces of a high dimensional data space that allow better clustering than original space CLIQUE can be considered as both density-based and grid-based It partitions each dimension into the same number of equal length interval It partitions an m-dimensional data space int ...
Data Mining Concepts
... In clustering there are no pre-defined classes. Self-similarity is used to group records. The user must attach meaning to the clusters formed Clustering often precedes some other data mining task, for example: – once customers are separated into clusters, a promotion might be carried out based o ...
... In clustering there are no pre-defined classes. Self-similarity is used to group records. The user must attach meaning to the clusters formed Clustering often precedes some other data mining task, for example: – once customers are separated into clusters, a promotion might be carried out based o ...
pptx
... – As correlation approaches 1 or -1, the distribution of correlation becomes non-normal • The 95% confidence interval for a correlation of 0.9 might include 1.1, but correlation can’t be greater than 1 ...
... – As correlation approaches 1 or -1, the distribution of correlation becomes non-normal • The 95% confidence interval for a correlation of 0.9 might include 1.1, but correlation can’t be greater than 1 ...
Integrated Customer Call Information
... Prod&Consult. “Who was my largest customer in terms of billing and what Cell Site carries their greatest amount of traffic, but has the highest number of call drop-outs?”. ...
... Prod&Consult. “Who was my largest customer in terms of billing and what Cell Site carries their greatest amount of traffic, but has the highest number of call drop-outs?”. ...
Data Mining
... supervised learning • Classification and regression use data with known values to train a machine learning model so that it can identify unknown values for other data entities with similar attributes. • Classification is used to identify Boolean (True/False) values. Regression is used to identify r ...
... supervised learning • Classification and regression use data with known values to train a machine learning model so that it can identify unknown values for other data entities with similar attributes. • Classification is used to identify Boolean (True/False) values. Regression is used to identify r ...
A micro-economic view of data mining.
... Lagrange multipliers and penalty functions [7]. However, from our point of view there is a major difference between the two: We assume that the feasible region D is basically endogenous to the enterprise, while the objective function f (x) is a function that reflects the ...
... Lagrange multipliers and penalty functions [7]. However, from our point of view there is a major difference between the two: We assume that the feasible region D is basically endogenous to the enterprise, while the objective function f (x) is a function that reflects the ...
CSE 5230 Data Mining
... organize their connectivity to optimize the spatial distribution of their responses within the layer?” » Can be used for analysis similar to clustering (more next week) » Teuvo Kohonen, “Self-organized formation of topologically correct feature maps”, Biological Cybernetics 43:59-69, ...
... organize their connectivity to optimize the spatial distribution of their responses within the layer?” » Can be used for analysis similar to clustering (more next week) » Teuvo Kohonen, “Self-organized formation of topologically correct feature maps”, Biological Cybernetics 43:59-69, ...
Enterprise resource planning
... application. Many DSSs are oriented toward individual decision support. There is growing interest in DSSs that directly support distributed decision making at the group, organization, and inter-organization levels. ...
... application. Many DSSs are oriented toward individual decision support. There is growing interest in DSSs that directly support distributed decision making at the group, organization, and inter-organization levels. ...
IOSR Journal of Computer Engineering (IOSR-JCE)
... FCM iteratively changes the centers of clusters to exact location in a dataset. When fuzzy logic is introduced in K-Means clustering algorithm, it becomes Fuzzy C-Means algorithm. FCM clustering is based on fuzzy behavior. The structure of FCM is basically similar to K-Means. In [20] experimental re ...
... FCM iteratively changes the centers of clusters to exact location in a dataset. When fuzzy logic is introduced in K-Means clustering algorithm, it becomes Fuzzy C-Means algorithm. FCM clustering is based on fuzzy behavior. The structure of FCM is basically similar to K-Means. In [20] experimental re ...
Mining Multivariate Spatiotemporal Patterns from Heterogeneous
... There are two approaches that one can follow to extract frequent multivariate-locations, namely (A1) identify the frequent locations based on spatial stay points, and analyze the variable data records that match the frequent locations, and (A2) identify the frequent locations on spatial stay points ...
... There are two approaches that one can follow to extract frequent multivariate-locations, namely (A1) identify the frequent locations based on spatial stay points, and analyze the variable data records that match the frequent locations, and (A2) identify the frequent locations on spatial stay points ...
problem of data analysis and forecasting using - CEUR
... Consequences This work provides the description, explanation and result of the decision tree method and its application to the real observations, using R programming language. It is compared with traditional approaches in order to define common features and drawbacks. Effectiveness of the method che ...
... Consequences This work provides the description, explanation and result of the decision tree method and its application to the real observations, using R programming language. It is compared with traditional approaches in order to define common features and drawbacks. Effectiveness of the method che ...
chap1_intro
... Origins of Data Mining Draws ideas from machine learning/AI, pattern recognition, statistics, and database systems ...
... Origins of Data Mining Draws ideas from machine learning/AI, pattern recognition, statistics, and database systems ...
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.