Assessing Loan Risks: A Data Mining Case Study
... regression equally well. More complex than other techniques, neural networks have often been described as a “black box” technology. They require setting numerous training parameters and, unlike decision trees, provide no easily understandable output. Naïve Bayes. This technique limits its inputs to ...
... regression equally well. More complex than other techniques, neural networks have often been described as a “black box” technology. They require setting numerous training parameters and, unlike decision trees, provide no easily understandable output. Naïve Bayes. This technique limits its inputs to ...
The Future of Predictive Modeling
... the amount needed to cover costs and build in some profit. As Niels Bohr, a Nobel Laureate in physics once said: “Prediction is very difficult, especially if it’s about the future.” Insurers have little to gain from attempting to build a perfect model under laboratory-type conditions. The rules of c ...
... the amount needed to cover costs and build in some profit. As Niels Bohr, a Nobel Laureate in physics once said: “Prediction is very difficult, especially if it’s about the future.” Insurers have little to gain from attempting to build a perfect model under laboratory-type conditions. The rules of c ...
a study : data mining models using business intelligence
... generated by the IT organizations as data is increasing day by day so its was getting difficult to find the better solutions because as we move to single approach of BI than its lacking with these following points: 1) Data may be uncertain 2) Accomplishing data may be exclusive/expansive. 3) Might n ...
... generated by the IT organizations as data is increasing day by day so its was getting difficult to find the better solutions because as we move to single approach of BI than its lacking with these following points: 1) Data may be uncertain 2) Accomplishing data may be exclusive/expansive. 3) Might n ...
Slide 1
... The new variables/dimensions Are linear combinations of the original ones Are uncorrelated with one another Orthogonal in original dimension space Capture as much of the original variance in the data as possible ...
... The new variables/dimensions Are linear combinations of the original ones Are uncorrelated with one another Orthogonal in original dimension space Capture as much of the original variance in the data as possible ...
data warehousing and mining
... JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD IV B.Tech (I-SEM) T P C ...
... JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD IV B.Tech (I-SEM) T P C ...
CHAPTER 5: Clarifying the Research Question through Secondary
... patterns for every record in the database, sampling should not be done. If the data warehouse is very large, processing power is limited, or speed is more important than complete ...
... patterns for every record in the database, sampling should not be done. If the data warehouse is very large, processing power is limited, or speed is more important than complete ...
From Big Data to Smart Data: Teaching Data Mining and Visualization
... It is clear that there must be more emphasis on knowledge and choice than on information. Patterns, clusters and classifications are at best the truth but in many cases we only have probable meaningful intelligence and have to make an adequate selection. Data driven analysis is a process by which ma ...
... It is clear that there must be more emphasis on knowledge and choice than on information. Patterns, clusters and classifications are at best the truth but in many cases we only have probable meaningful intelligence and have to make an adequate selection. Data driven analysis is a process by which ma ...
Applications of Spatio-Temporal Data Mining to North
... Spatio-temporal data occur in a variety of forms [3,6,8,12] either as a series of discrete snapshots or as a continuous representation that is obtained by some interpolation method [9]. Spatiotemporal objects include moving objects [1], epidemic regions [16], and numerous changing geographic feature ...
... Spatio-temporal data occur in a variety of forms [3,6,8,12] either as a series of discrete snapshots or as a continuous representation that is obtained by some interpolation method [9]. Spatiotemporal objects include moving objects [1], epidemic regions [16], and numerous changing geographic feature ...
Data Mining Approaches for Intrusion Detection
... – start time and duration – participating hosts and ports (applications) – statistics (e.g., # of bytes) – flag: “normal” or a connection/termination error – protocol: TCP or UDP – Collection of temporal features extracted using data mining, example in PortScan multiple ...
... – start time and duration – participating hosts and ports (applications) – statistics (e.g., # of bytes) – flag: “normal” or a connection/termination error – protocol: TCP or UDP – Collection of temporal features extracted using data mining, example in PortScan multiple ...
Slide 1
... targeted at specific customers based on their buying behavior; • Can be used to collect customer information from your Web site; • Should be organized to enable you to search the database using queries; • Should be compatible with database software that will enhance analysis. Chapter Three ...
... targeted at specific customers based on their buying behavior; • Can be used to collect customer information from your Web site; • Should be organized to enable you to search the database using queries; • Should be compatible with database software that will enhance analysis. Chapter Three ...
Data Mining, Neural Networks, and Genetic Programming
... – What is the difference between DM and data warehousing? – What is the difference between DM and record retrieval? ...
... – What is the difference between DM and data warehousing? – What is the difference between DM and record retrieval? ...
training data - WordPress.com
... being distant from the rest of the data (definition of “distant” is deliberately vague) Outliers can have disproportionate influence on models (a problem if it is spurious) An important step in data pre-processing is detecting outliers Once detected, domain knowledge is required to determine i ...
... being distant from the rest of the data (definition of “distant” is deliberately vague) Outliers can have disproportionate influence on models (a problem if it is spurious) An important step in data pre-processing is detecting outliers Once detected, domain knowledge is required to determine i ...
Data Mining: An Overview of Methods and Technologies for Increasing Profits in Direct Marketing
... between the independent variables and the log of the odds of the dependent variable, transformations can be used to make the independent variables more linear. Examples of transformations include the square, cube, square root, cube root, and the log. Some complex methods have been developed to deter ...
... between the independent variables and the log of the odds of the dependent variable, transformations can be used to make the independent variables more linear. Examples of transformations include the square, cube, square root, cube root, and the log. Some complex methods have been developed to deter ...
Project Presentation - University of Calgary
... clusters from the vertices in that order, first encompassing first order neighbors, then second order neighbors and so on. The growth stops when the boundary of the cluster is determined. Noise removal phase: The algorithm identifies noise as sparse clusters. They can be easily eliminated by removin ...
... clusters from the vertices in that order, first encompassing first order neighbors, then second order neighbors and so on. The growth stops when the boundary of the cluster is determined. Noise removal phase: The algorithm identifies noise as sparse clusters. They can be easily eliminated by removin ...
OUTLIER DETECTION AND SYSTEM ANALYSIS USING MINING
... The intrusion detection system has been implemented using various data mining techniques which help user to identify or classify various attacks or number of intrusion in a network. KDD dataset is one of the popular dataset to test classification technique s. In this paper our work is done on analys ...
... The intrusion detection system has been implemented using various data mining techniques which help user to identify or classify various attacks or number of intrusion in a network. KDD dataset is one of the popular dataset to test classification technique s. In this paper our work is done on analys ...
Android Application to Predict and Suggest Measures for Diabetes
... of cases. And also each cases are assigned with weights to take into account the unknown attributes values [17]. At the beginning, only the root is present, associated with the whole training set T S and with all case weights equal to 1:0. At each node the following divide and conquer method, the al ...
... of cases. And also each cases are assigned with weights to take into account the unknown attributes values [17]. At the beginning, only the root is present, associated with the whole training set T S and with all case weights equal to 1:0. At each node the following divide and conquer method, the al ...
An Efficient Classification Algorithm for Real Estate domain
... demographic details). Each of the tests is done on three test modes. The results are generated using WEKA 3-6-2, open source software for regression analysis and data mining. The following are the findings, as per Table 1: As per traditional classification, in case of complete dataset, Training te ...
... demographic details). Each of the tests is done on three test modes. The results are generated using WEKA 3-6-2, open source software for regression analysis and data mining. The following are the findings, as per Table 1: As per traditional classification, in case of complete dataset, Training te ...
IFIS Uni Lübeck - Universität zu Lübeck
... In general, this is an unsolved problem. However there are many approximate methods. In the next few slides we will see an example. ...
... In general, this is an unsolved problem. However there are many approximate methods. In the next few slides we will see an example. ...
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.