Introducing Economic Order Quantity Model for Inventory Control in
... compared in this paper. The third model includes greater specific procedures which can lead to an optimal solution due to its lowest cost. Pradip Kumar Bala et al. in [5] argue that minimum error for demand forecast lead to success in supply chain management. With the advent of data mining systems, ...
... compared in this paper. The third model includes greater specific procedures which can lead to an optimal solution due to its lowest cost. Pradip Kumar Bala et al. in [5] argue that minimum error for demand forecast lead to success in supply chain management. With the advent of data mining systems, ...
Document
... algorithmic-based analysis – the start of ~ non-parametric analysis ~ ‘local methods’ – Thus distance becomes important. ...
... algorithmic-based analysis – the start of ~ non-parametric analysis ~ ‘local methods’ – Thus distance becomes important. ...
A comparison of model-based and regression classification
... type into known types. Many classification methods have been proposed in the chemometrics, statistical and computer science literature. Model-based classification methods have been developed from a statistical modelling viewpoint. This approach allows for uncertainty in the classification procedure ...
... type into known types. Many classification methods have been proposed in the chemometrics, statistical and computer science literature. Model-based classification methods have been developed from a statistical modelling viewpoint. This approach allows for uncertainty in the classification procedure ...
Density-Linked Clustering
... OPTICS consists of two values for each object, the core distance and the reachability distance w.r.t. parameters minPts ∈ N and ε ∈ R. The clustering result can be displayed in a so-called reachability plot that is more appropriate for very large data sets than a dendrogram. A reachability plot con ...
... OPTICS consists of two values for each object, the core distance and the reachability distance w.r.t. parameters minPts ∈ N and ε ∈ R. The clustering result can be displayed in a so-called reachability plot that is more appropriate for very large data sets than a dendrogram. A reachability plot con ...
Advances in Environmental Biology Abouzar Qorbani,
... one of the efficient and simple algorithms in data mining. One of the key points in this method is independency of features towards each other, that is in this model it is assumed that the features are separated from each other and there is dependency among them. Although this assumption is unreal i ...
... one of the efficient and simple algorithms in data mining. One of the key points in this method is independency of features towards each other, that is in this model it is assumed that the features are separated from each other and there is dependency among them. Although this assumption is unreal i ...
IOSR Journal of Computer Engineering (IOSR-JCE)
... and revised-type grouping method. The K-Means is one of the most widely used partitional clustering methods due to its simplicity, versatility, efficiency, empirical success and ease of implementation. This is evidenced by more than hundreds of publications over the last fifty five years that extend ...
... and revised-type grouping method. The K-Means is one of the most widely used partitional clustering methods due to its simplicity, versatility, efficiency, empirical success and ease of implementation. This is evidenced by more than hundreds of publications over the last fifty five years that extend ...
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.