Data Mining as a Tool for Environmental Scientists
... with in a reasonable way, which is not unusual in data mining context, it may be convenient to apply a data reduction method. This kind of technique consists of finding some set with the minimum number of variables that captures the information contained in the original data set. This may be accompl ...
... with in a reasonable way, which is not unusual in data mining context, it may be convenient to apply a data reduction method. This kind of technique consists of finding some set with the minimum number of variables that captures the information contained in the original data set. This may be accompl ...
Data Mining for Multi-agent Fuzzy Decision Tree Structure and Rules
... 2 A brief introduction to fuzzy sets, fuzzy logic and the fuzzy RM The RM must be able to deal with linguistically imprecise information provided by an expert. Also, the RM must control a number of assets and be flexible enough to rapidly adapt to change. The above requirements suggest an approach b ...
... 2 A brief introduction to fuzzy sets, fuzzy logic and the fuzzy RM The RM must be able to deal with linguistically imprecise information provided by an expert. Also, the RM must control a number of assets and be flexible enough to rapidly adapt to change. The above requirements suggest an approach b ...
Data warehouse
... – define dimension time as time in cube sales – define dimension item as item in cube sales – define dimension shipper as (shipper_key, shipper_name, location as location in cube sales, shipper_type) – define dimension from_location as location in cube sales – define dimension to_location as locatio ...
... – define dimension time as time in cube sales – define dimension item as item in cube sales – define dimension shipper as (shipper_key, shipper_name, location as location in cube sales, shipper_type) – define dimension from_location as location in cube sales – define dimension to_location as locatio ...
Data Mining - The Clute Institute
... Recently data mining has become more popular in the information industry. It is due to the availability of huge amounts of data. Industry needs turning such data into useful information and knowledge. This information and knowledge can be used in many applications ranging from business management, p ...
... Recently data mining has become more popular in the information industry. It is due to the availability of huge amounts of data. Industry needs turning such data into useful information and knowledge. This information and knowledge can be used in many applications ranging from business management, p ...
Multiple Linear Regression in Data Mining
... 4. Homoskedasticity The standard deviation of εi equals the same (unknown) value, σ, for i = 1, 2, . . . , n. ...
... 4. Homoskedasticity The standard deviation of εi equals the same (unknown) value, σ, for i = 1, 2, . . . , n. ...
Efficient Implementation of FP Growth Algorithm
... management that can be explained as optimization of medical center processes in the form of medical, management and cost benefits analysis. However, the major issues regarding medical data processes are the standards, strategic plans, treatment, diagnoses, medical tests, and finally quality of data. ...
... management that can be explained as optimization of medical center processes in the form of medical, management and cost benefits analysis. However, the major issues regarding medical data processes are the standards, strategic plans, treatment, diagnoses, medical tests, and finally quality of data. ...
Drawbacks and solutions of applying association rule mining in
... algorithm [37], which automatically resolves the problem of balance between these two parameters, maximizing the probability of making an accurate prediction for the data set. In order to achieve this, a parameter called the exact expected predictive accuracy is defined and calculated using the Baye ...
... algorithm [37], which automatically resolves the problem of balance between these two parameters, maximizing the probability of making an accurate prediction for the data set. In order to achieve this, a parameter called the exact expected predictive accuracy is defined and calculated using the Baye ...
A Condensation Approach to Privacy Preserving Data Mining
... corresponding generalized value. We note that the choice of the best generalization hierarchy and strategy in the k-anonymity model is highly specific to a particular application, and is in fact dependent upon the user or domain expert. In many applications and data sets, it may be difficult to obtain ...
... corresponding generalized value. We note that the choice of the best generalization hierarchy and strategy in the k-anonymity model is highly specific to a particular application, and is in fact dependent upon the user or domain expert. In many applications and data sets, it may be difficult to obtain ...
Application of Data Mining Techniques on Heart Disease Prediction
... The work of Amin et al. [2], Genetic Neural Network Based Data Mining in Prediction of Heart Disease Using Risk Factors developed an intelligent data mining system based on genetic algorithm. To transform data into useful form, encoding was done between a range [−1, 1]. Neural Network Weight Optimiz ...
... The work of Amin et al. [2], Genetic Neural Network Based Data Mining in Prediction of Heart Disease Using Risk Factors developed an intelligent data mining system based on genetic algorithm. To transform data into useful form, encoding was done between a range [−1, 1]. Neural Network Weight Optimiz ...
DATA QUALITY IN THE CONTEXT OF CUSTOMER SEGMENTATION
... types of input data in mind. This is also true for clustering algorithms. Modern research focuses on scalability of algorithms, high-dimensional clustering techniques, the effectiveness of clustering complex shapes of data, proper data preparation and selection, and methods for clustering mixed nume ...
... types of input data in mind. This is also true for clustering algorithms. Modern research focuses on scalability of algorithms, high-dimensional clustering techniques, the effectiveness of clustering complex shapes of data, proper data preparation and selection, and methods for clustering mixed nume ...
Lab Project - Department of Computer Science at CCSU
... Web document clustering is an important application of Machine Learning for the Web. A clustering system can be useful in web search for grouping search results into closely related sets of documents. Clustering can improve similarity search by focusing on sets of relevant documents and hierarchical ...
... Web document clustering is an important application of Machine Learning for the Web. A clustering system can be useful in web search for grouping search results into closely related sets of documents. Clustering can improve similarity search by focusing on sets of relevant documents and hierarchical ...
Improving Categorical DataClusterinq Algorithm by
... attribute values that are less common in the population. In other words, similarity among objects is decided by the un-commonality of their attribute value matches. Similarity computed using the heuristic of weighting uncommon attribute value matches helps to define more cohesive, tight clusters whe ...
... attribute values that are less common in the population. In other words, similarity among objects is decided by the un-commonality of their attribute value matches. Similarity computed using the heuristic of weighting uncommon attribute value matches helps to define more cohesive, tight clusters whe ...
A Parallel Clustering Method Study Based on MapReduce
... problems’ feature variable vector is in high dimension. Too many input variable will increase the computation cost of SVM. Feature extraction can decrease the dimension of input and decrease the computation cost efficiently. Many feature extraction methods have been proposed, such as Principal Compo ...
... problems’ feature variable vector is in high dimension. Too many input variable will increase the computation cost of SVM. Feature extraction can decrease the dimension of input and decrease the computation cost efficiently. Many feature extraction methods have been proposed, such as Principal Compo ...
Data Mining Tutorial
... If the Life Line is long and deep, then this represents a long life full of vitality and health. A short line, if strong and deep, also shows great vitality in your life and the ability to overcome health problems. However, if the line is short and shallow, then your life may have the tendency to b ...
... If the Life Line is long and deep, then this represents a long life full of vitality and health. A short line, if strong and deep, also shows great vitality in your life and the ability to overcome health problems. However, if the line is short and shallow, then your life may have the tendency to b ...
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.