Efficiently Mining Asynchronous Periodic Patterns
... to a certain threshold. Two parameters, namely min_rep and max_dis are employed to qualify valid patterns and the symbol subsequence containing it. However their model has several problems such as lack of finding multiple events at one time slot and lack of finding successive nonoverlapped segments. ...
... to a certain threshold. Two parameters, namely min_rep and max_dis are employed to qualify valid patterns and the symbol subsequence containing it. However their model has several problems such as lack of finding multiple events at one time slot and lack of finding successive nonoverlapped segments. ...
Data Mining - PhD in Information Engineering
... Amount of information gained by knowing the value of ! the attribute Entropy of distribution before the split ...
... Amount of information gained by knowing the value of ! the attribute Entropy of distribution before the split ...
An Efficient k-Means Clustering Algorithm Using Simple Partitioning
... does not require more than two scans of the dataset. Similar to Alsabti’s partition method for finding splitting points, for two dimensional data we determine the minimum and maximum values of the data along each di- ...
... does not require more than two scans of the dataset. Similar to Alsabti’s partition method for finding splitting points, for two dimensional data we determine the minimum and maximum values of the data along each di- ...
Entropy-based Subspace Clustering for Mining Numerical Data
... existence of outliers. It should not require the users to specify some parameters on which the users would have diculty to decide. For instance, the K-means algorithm requires the user to specify the number of clusters, which is often not known to the user. Finally there should be a meaningful and ...
... existence of outliers. It should not require the users to specify some parameters on which the users would have diculty to decide. For instance, the K-means algorithm requires the user to specify the number of clusters, which is often not known to the user. Finally there should be a meaningful and ...
K-NEAREST NEIGHBOR BASED DBSCAN CLUSTERING
... important technique in data mining. The groups that are designed depending on the density are flexible to understand and do not restrict itself to the outlines of clusters. DBSCAN Algorithm is one of the density grounded clustering approach which is employed in this paper. The author addressed two d ...
... important technique in data mining. The groups that are designed depending on the density are flexible to understand and do not restrict itself to the outlines of clusters. DBSCAN Algorithm is one of the density grounded clustering approach which is employed in this paper. The author addressed two d ...
T RETAILER PROMOTION PLANNING: IMPROVING FORECAST ACCURACY AND INTERPRETABILITY
... • “TPR” identifies the level of the Temporary Price Reduction. Promotions usually involve some itemprice reduction. Values for this variable have been generalized to a set of five possible discrete values: None, Low, Medium, High, and Very High. • “Mfr” identifies the manufacturer of the given produ ...
... • “TPR” identifies the level of the Temporary Price Reduction. Promotions usually involve some itemprice reduction. Values for this variable have been generalized to a set of five possible discrete values: None, Low, Medium, High, and Very High. • “Mfr” identifies the manufacturer of the given produ ...
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.