Mining High Dimensional Data Using Attribute Clustering
... when the number of features is very big. So, we will focus on attribute based cluster in this paper. With respect to the attribute cluster based selection techniques, the cluster analysis application has been demonstrated to be more effective than previous feature selection algorithms and Dhillon et ...
... when the number of features is very big. So, we will focus on attribute based cluster in this paper. With respect to the attribute cluster based selection techniques, the cluster analysis application has been demonstrated to be more effective than previous feature selection algorithms and Dhillon et ...
Mining High Quality Association Rules Using - CEUR
... Since the association rule mining problem was proposed by (Agrawal, Imielinski, & Swami 1993), several algorithms have been developed. In the most commonly used approach, the rule generation process is split into two separate steps. The first step includes applying the minimum support to find all fr ...
... Since the association rule mining problem was proposed by (Agrawal, Imielinski, & Swami 1993), several algorithms have been developed. In the most commonly used approach, the rule generation process is split into two separate steps. The first step includes applying the minimum support to find all fr ...
spatio-temporal structures characterization based on multi
... jective representation by machine learning methods. The advantage of such a concept is that it is free of the application specificity and adapts to the user’s query. Due to the large amount of information contained in SITS, the quantity of information recquired to represent data is a crucial point. ...
... jective representation by machine learning methods. The advantage of such a concept is that it is free of the application specificity and adapts to the user’s query. Due to the large amount of information contained in SITS, the quantity of information recquired to represent data is a crucial point. ...
The Association Mining Rules - Market Basket Analysis
... The most popular method to bring out co-occurrence of items in a database of transactions is the association rule. The association rule is used in application such as market baskets analysis to measure the associations between products and customers. The association pattern technique used in this st ...
... The most popular method to bring out co-occurrence of items in a database of transactions is the association rule. The association rule is used in application such as market baskets analysis to measure the associations between products and customers. The association pattern technique used in this st ...
Review of Existing Methods for Finding Initial Clusters in K
... 3.7 Automatic Initialization of Means Samarjeet Borah and Mrinal Kanti Ghose proposed Automatic Initialization of Means (AIM) algorithm. In this method the original dataset D is first copied to a temporary dataset T. The algorithm is required to run n times i.e. equal to the number of objects in the ...
... 3.7 Automatic Initialization of Means Samarjeet Borah and Mrinal Kanti Ghose proposed Automatic Initialization of Means (AIM) algorithm. In this method the original dataset D is first copied to a temporary dataset T. The algorithm is required to run n times i.e. equal to the number of objects in the ...
Discriminant Diagnostics
... may distort the analysis because of high influence or unusual values ·of the dependent or independent variables. This investigation may result in removing the unusual observation from the analysis or in giving it reduced weight. Similar concerns exist in two group Discriminant Analysis: because an o ...
... may distort the analysis because of high influence or unusual values ·of the dependent or independent variables. This investigation may result in removing the unusual observation from the analysis or in giving it reduced weight. Similar concerns exist in two group Discriminant Analysis: because an o ...
Association Rule Generation in Streams
... be thousands or millions of records that have to be read and to extract the rules for, but in the past user would repeat the whole procedure, which is time –consuming in addition to its lack of efficiency for new data, or there is a need to modify or delete some or all the existing set of data durin ...
... be thousands or millions of records that have to be read and to extract the rules for, but in the past user would repeat the whole procedure, which is time –consuming in addition to its lack of efficiency for new data, or there is a need to modify or delete some or all the existing set of data durin ...
Expectation–maximization algorithm
In statistics, an expectation–maximization (EM) algorithm is an iterative method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.