PPT Format - Karim El
... Introduction to Data Mining Neighborhoods Basic idea: For a new problem, look for the similar problems (neighborhoods) that have been solved Key point: find the neighborhood Calculate the distance: how far is good to be considered as a neighbor? Which class the new problem belong to? Large co ...
... Introduction to Data Mining Neighborhoods Basic idea: For a new problem, look for the similar problems (neighborhoods) that have been solved Key point: find the neighborhood Calculate the distance: how far is good to be considered as a neighbor? Which class the new problem belong to? Large co ...
Machine Learning
... How to represent the inputs? How to remove the irrelevant information from the input representation? How to reduce the redundancy of the input representation? ...
... How to represent the inputs? How to remove the irrelevant information from the input representation? How to reduce the redundancy of the input representation? ...
Business Intelligence: A Design Science Perspective p
... on the loan application such as the ratio of the l loan amount to iincome and d the h iinterest rate off the loan. To develop the model they have gathered this data for 1000 past completed loans. Of these loans 700 have been paid in full (Default = 0), 700 have defaulted (Default = 1). ...
... on the loan application such as the ratio of the l loan amount to iincome and d the h iinterest rate off the loan. To develop the model they have gathered this data for 1000 past completed loans. Of these loans 700 have been paid in full (Default = 0), 700 have defaulted (Default = 1). ...
Implementation of Combined Approach of Prototype Shikha Gadodiya
... adapted from an example in the software package aML, and is based on a longitudinal survey conducted in the U.S.A. It is available as data mining dataset in open source. The KEEL tool’s inbuilt methods DROP3 and CPruner were applied on this dataset. The KEEL gui, experimental setup and statistical r ...
... adapted from an example in the software package aML, and is based on a longitudinal survey conducted in the U.S.A. It is available as data mining dataset in open source. The KEEL tool’s inbuilt methods DROP3 and CPruner were applied on this dataset. The KEEL gui, experimental setup and statistical r ...
Brief Application Description - Bilkent University Computer
... Focusing (AF), (Bhandari, 1995). An overall distribution of an attribute is compared with the distribution of this attribute for various subsets of the data. If a certain subset of data has a characteristically different distribution for the focus attribute, then that combination of attributes, (the ...
... Focusing (AF), (Bhandari, 1995). An overall distribution of an attribute is compared with the distribution of this attribute for various subsets of the data. If a certain subset of data has a characteristically different distribution for the focus attribute, then that combination of attributes, (the ...
ibm_rochester_talk_may_2005
... – Let = 1/4. In other words, each transaction needs to have 3/4 (75%) of the items. – X = {i1, i2, i3, i4} and Y = {i5, i6, i7, i8} are both ETIs with a support of 4. ...
... – Let = 1/4. In other words, each transaction needs to have 3/4 (75%) of the items. – X = {i1, i2, i3, i4} and Y = {i5, i6, i7, i8} are both ETIs with a support of 4. ...
Lecture Notes - L3S Research Center
... Valid: hold on new data with some certainty Useful: should be possible to act on the item Unexpected: non-‐obvious to the system Understandable: humans should be able to interpret the pa;e ...
... Valid: hold on new data with some certainty Useful: should be possible to act on the item Unexpected: non-‐obvious to the system Understandable: humans should be able to interpret the pa;e ...
Data Mining for Business Intelligence in CRM System
... Section 2 described the related work in CRM Data Mining. Section 3 provides a general description of the data used. Section 4 described the process stage of data used. Section 5 reports our experimental analysis of data mining methods applied on CRM data set. Finally, conclude this paper with anout ...
... Section 2 described the related work in CRM Data Mining. Section 3 provides a general description of the data used. Section 4 described the process stage of data used. Section 5 reports our experimental analysis of data mining methods applied on CRM data set. Finally, conclude this paper with anout ...
application of data mining techniques for the development of new
... fields and more recently also in geotechnics with good results in different applications. They are adequate as an advanced technique for analysing large and complex databases that can be built with geotechnical information within the framework of an overall process of Knowledge Discovery in Database ...
... fields and more recently also in geotechnics with good results in different applications. They are adequate as an advanced technique for analysing large and complex databases that can be built with geotechnical information within the framework of an overall process of Knowledge Discovery in Database ...
class discovery
... They consist of layers of nodes that send out ”signals” based probabilistically on input signals Most known uses are classifications, i.e., with learning sets ...
... They consist of layers of nodes that send out ”signals” based probabilistically on input signals Most known uses are classifications, i.e., with learning sets ...
Visual Data Mining: Integrating Machine Learning with Information
... traditional projection methods such as Principle Component Analysis (PCA) [4], Factor Analysis [13], Multidimensional Scaling [42], Sammon’s mapping [29], Self-Organizing Map (SOM) [18], and FastMap [12] are all used in the knowledge discovery and data mining domain [15] [37] [38] [19]. For many rea ...
... traditional projection methods such as Principle Component Analysis (PCA) [4], Factor Analysis [13], Multidimensional Scaling [42], Sammon’s mapping [29], Self-Organizing Map (SOM) [18], and FastMap [12] are all used in the knowledge discovery and data mining domain [15] [37] [38] [19]. For many rea ...
Facilities Management
... mining process is performed locally on each data server. The size of results of the first two accomplished DM processes are compared. The smaller one is migrated to the larger one. The knowledge integrator agent integrates the results of these two data servers. This process is repeated until all in ...
... mining process is performed locally on each data server. The size of results of the first two accomplished DM processes are compared. The smaller one is migrated to the larger one. The knowledge integrator agent integrates the results of these two data servers. This process is repeated until all in ...
Data Mining with The SAS System
... • Describes how they fit into a linear models (regression/ANOVA) framework. • Results from this procedure can be passed to the Neural Network and Data Splits tools or to any other procedure in the SAS System. ...
... • Describes how they fit into a linear models (regression/ANOVA) framework. • Results from this procedure can be passed to the Neural Network and Data Splits tools or to any other procedure in the SAS System. ...
LECTURE PLAN Lecture Hour Contents Learning
... Classic parametric tests- paired and unpaired. Non parametric tests- bootstrap analysis Multiplicity of testing- bonferroni adjustment and ANOVA Similarity analysis of relationships between genes using correlation coefficient, rank coefficient and Euclidean distance Hierarchial clustering & Linkage ...
... Classic parametric tests- paired and unpaired. Non parametric tests- bootstrap analysis Multiplicity of testing- bonferroni adjustment and ANOVA Similarity analysis of relationships between genes using correlation coefficient, rank coefficient and Euclidean distance Hierarchial clustering & Linkage ...
DMW - sitams
... To analyze the data, identify the problems, and choose the relevant models and algorithms to apply. To familiarize the student with the concepts of data warehouse and data mining, To make the student acquaint with the tools and techniques used for Knowledge Discovery in Databases, and other data rep ...
... To analyze the data, identify the problems, and choose the relevant models and algorithms to apply. To familiarize the student with the concepts of data warehouse and data mining, To make the student acquaint with the tools and techniques used for Knowledge Discovery in Databases, and other data rep ...
AyBi199_Lec1 - Caltech Astronomy
... • Many (most? all?) complex systems a priori cannot be described analytically, but only computationally • What does it mean if a theory is not analytical, but expressed as an algorithm, or a computation? – It has to be analytical at some “atomic” level (?) – Even if we manage to reproduce numericall ...
... • Many (most? all?) complex systems a priori cannot be described analytically, but only computationally • What does it mean if a theory is not analytical, but expressed as an algorithm, or a computation? – It has to be analytical at some “atomic” level (?) – Even if we manage to reproduce numericall ...
Improving the orthogonal range search k -windows algorithm
... Moreover, we have applied the above three versions of k-windows algorithm in multidimensional MagnetoEncephaloGram (MEG) signals which are generated from the ionic micro-currents of the brain and originated at the cellular level [1]. The MEG analysis can provide information of vital importance for t ...
... Moreover, we have applied the above three versions of k-windows algorithm in multidimensional MagnetoEncephaloGram (MEG) signals which are generated from the ionic micro-currents of the brain and originated at the cellular level [1]. The MEG analysis can provide information of vital importance for t ...
IOSR Journal of Computer Engineering (IOSR-JCE)
... tool in the data mining. Clustering algorithms are mainly divided into two categories: Hierarchical algorithms and Partition algorithms. A hierarchical clustering algorithm divides the given data set into smaller subsets in hierarchical fashion. A partition clustering algorithm partition the data se ...
... tool in the data mining. Clustering algorithms are mainly divided into two categories: Hierarchical algorithms and Partition algorithms. A hierarchical clustering algorithm divides the given data set into smaller subsets in hierarchical fashion. A partition clustering algorithm partition the data se ...
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.