Lecture 3b
... Reason: data has not been collected for mining it Result: errors and omissions that don’t affect original purpose of data (e.g. age of customer) Typographical errors in nominal attributes values need to be checked for consistency Typographical and measurement errors in numeric attributes ...
... Reason: data has not been collected for mining it Result: errors and omissions that don’t affect original purpose of data (e.g. age of customer) Typographical errors in nominal attributes values need to be checked for consistency Typographical and measurement errors in numeric attributes ...
Course outline
... To develop conceptual and theoretical understanding of the data mining process To provide hands-on experience in the implementation and evaluation of data mining algorithms and tools To develop interest in data mining research ...
... To develop conceptual and theoretical understanding of the data mining process To provide hands-on experience in the implementation and evaluation of data mining algorithms and tools To develop interest in data mining research ...
slides
... • Relationship from queried vertices to desired vertices PQ – e.g., “passengers on the flight” ...
... • Relationship from queried vertices to desired vertices PQ – e.g., “passengers on the flight” ...
Medical Data Mining Techniques for Health Care Systems
... [31] to diagnose diabetes. This paper used the maximum and minimum relationship to deal with uncertainty availed in the dataset. Data sets of forty patients were collected to produce this relationship. ...
... [31] to diagnose diabetes. This paper used the maximum and minimum relationship to deal with uncertainty availed in the dataset. Data sets of forty patients were collected to produce this relationship. ...
An Application in SPSS Clementine Based on the
... Durdu (2012), in his study, a structure is developed which can be a basement for activities of management of customer relationship by using data mining tools and applications. In the study customer main data and sales operations are transformed into usable and valuable data that can be used in manag ...
... Durdu (2012), in his study, a structure is developed which can be a basement for activities of management of customer relationship by using data mining tools and applications. In the study customer main data and sales operations are transformed into usable and valuable data that can be used in manag ...
Knowledge Discovery in
... fitting the training data, resulting in reduced prediction accuracy on unseen data. Model-evaluation criteria are quantitative statements (or fit functions) of how well a particular pattern (a model and its parameters) meets the goals of the KDD process. For example, predictive models are often judg ...
... fitting the training data, resulting in reduced prediction accuracy on unseen data. Model-evaluation criteria are quantitative statements (or fit functions) of how well a particular pattern (a model and its parameters) meets the goals of the KDD process. For example, predictive models are often judg ...
Association Analysis Techniques for Bioinformatics Problems
... early on in the search process. Efforts to date have created a well-developed conceptual (theoretical) foundation [64] and an efficient set of algorithms [2,20]. The framework has been extended well beyond the original application to market basket data to encompass new applications [8,24,23,57]. Despit ...
... early on in the search process. Efforts to date have created a well-developed conceptual (theoretical) foundation [64] and an efficient set of algorithms [2,20]. The framework has been extended well beyond the original application to market basket data to encompass new applications [8,24,23,57]. Despit ...
large synthetic data sets to compare different data mining methods
... Our goal was to generate synthetic datasets which would help to stress all the advantages and disadvantages of the chosen data mining methods. There are some known characteristics of datasets which can make the classification difficult for almost every data mining method. These are noise, crosstalk ...
... Our goal was to generate synthetic datasets which would help to stress all the advantages and disadvantages of the chosen data mining methods. There are some known characteristics of datasets which can make the classification difficult for almost every data mining method. These are noise, crosstalk ...
Data Mining of Occupant Behavior in Office Buildings
... occupancy presence. Raw data were transformed into more significant pre-processed data representing invariant attributes of the data set and mined though a decision tree model with the goal to predict the value of a label attribute (occupancy) based on predictor attributes (Season, Day of the week, ...
... occupancy presence. Raw data were transformed into more significant pre-processed data representing invariant attributes of the data set and mined though a decision tree model with the goal to predict the value of a label attribute (occupancy) based on predictor attributes (Season, Day of the week, ...
CLIP4 Inductive Machine Learning Algorithm
... • Faster data processing that allows it to analyze large data sets • Generation of more accurate rules • Ability to work with missing-value data, multi-class problems, to discretize the data • Easy to use software ...
... • Faster data processing that allows it to analyze large data sets • Generation of more accurate rules • Ability to work with missing-value data, multi-class problems, to discretize the data • Easy to use software ...
Big Data Mining: A Study
... B. Classification Classification is the process of assigning an object to a certain class based on its similarity to previous examples of other objects. It can be done with reference to original data or based on a model of that data. Classification is similar to clustering in that it also segments c ...
... B. Classification Classification is the process of assigning an object to a certain class based on its similarity to previous examples of other objects. It can be done with reference to original data or based on a model of that data. Classification is similar to clustering in that it also segments c ...
CS 524 – High Performance Computing
... To develop conceptual and theoretical understanding of the data mining process To provide hands-on experience in the implementation and evaluation of data mining algorithms and tools To develop interest in data mining research ...
... To develop conceptual and theoretical understanding of the data mining process To provide hands-on experience in the implementation and evaluation of data mining algorithms and tools To develop interest in data mining research ...
Lecture slides
... • The examples are stored verbatim, and a distance function is used to determine which members of the database are closest to a new example with a desirable prediction. • The K-Nearest Neighbor (KNN) is the most representative method. • They are good candidates to be improved through data reduction ...
... • The examples are stored verbatim, and a distance function is used to determine which members of the database are closest to a new example with a desirable prediction. • The K-Nearest Neighbor (KNN) is the most representative method. • They are good candidates to be improved through data reduction ...
Data mining Intro
... • Disadvantages of decision trees — Limited to one output attribute — Decision tree algorithms are not so stable ...
... • Disadvantages of decision trees — Limited to one output attribute — Decision tree algorithms are not so stable ...
An Interactive Data Mining Framework for EarthCube
... sophisticated new tools for interactive visualization and mining of multiple datasets. Such an effort will require an intellectual partnership between the geosciences and computer science research communities. At Virginia Tech such a collaborative partnership already exists between the SuperDARN HF ...
... sophisticated new tools for interactive visualization and mining of multiple datasets. Such an effort will require an intellectual partnership between the geosciences and computer science research communities. At Virginia Tech such a collaborative partnership already exists between the SuperDARN HF ...
Attribute Space Visualization of Demographic Change
... data. The SOM is then ready for application using other ndimensional data. Refer to Teuvo Kohonen’s monograph [5] for an in-depth discussion of SOM principles and applications. Numerous brief introductions to the method are found elsewhere, including in geographic contexts [3 , 13, 14]. Most geograp ...
... data. The SOM is then ready for application using other ndimensional data. Refer to Teuvo Kohonen’s monograph [5] for an in-depth discussion of SOM principles and applications. Numerous brief introductions to the method are found elsewhere, including in geographic contexts [3 , 13, 14]. Most geograp ...
Chapter 1 - Cios Lab
... from Other Approaches? Data mining is not just an “umbrella” term coined for the purpose of making sense of data. The major distinguishing characteristic of DM is that it is data driven, as opposed to other methods that are often model driven. In statistics, researchers frequently deal with the prob ...
... from Other Approaches? Data mining is not just an “umbrella” term coined for the purpose of making sense of data. The major distinguishing characteristic of DM is that it is data driven, as opposed to other methods that are often model driven. In statistics, researchers frequently deal with the prob ...
s - Community Grids Lab
... • MDS and GTM are highly memory and time consuming process for large dataset such as millions of data points • MDS requires O(N2) and GTM does O(KN) (N is the number of data points and K is the number of latent variables) • Training only for sampled data and interpolating for out-ofsample set can im ...
... • MDS and GTM are highly memory and time consuming process for large dataset such as millions of data points • MDS requires O(N2) and GTM does O(KN) (N is the number of data points and K is the number of latent variables) • Training only for sampled data and interpolating for out-ofsample set can im ...
Cluster analysis
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics.Cluster analysis itself is not one specific algorithm, but the general task to be solved. It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with small distances among the cluster members, dense areas of the data space, intervals or particular statistical distributions. Clustering can therefore be formulated as a multi-objective optimization problem. The appropriate clustering algorithm and parameter settings (including values such as the distance function to use, a density threshold or the number of expected clusters) depend on the individual data set and intended use of the results. Cluster analysis as such is not an automatic task, but an iterative process of knowledge discovery or interactive multi-objective optimization that involves trial and failure. It will often be necessary to modify data preprocessing and model parameters until the result achieves the desired properties.Besides the term clustering, there are a number of terms with similar meanings, including automatic classification, numerical taxonomy, botryology (from Greek βότρυς ""grape"") and typological analysis. The subtle differences are often in the usage of the results: while in data mining, the resulting groups are the matter of interest, in automatic classification the resulting discriminative power is of interest. This often leads to misunderstandings between researchers coming from the fields of data mining and machine learning, since they use the same terms and often the same algorithms, but have different goals.Cluster analysis was originated in anthropology by Driver and Kroeber in 1932 and introduced to psychology by Zubin in 1938 and Robert Tryon in 1939 and famously used by Cattell beginning in 1943 for trait theory classification in personality psychology.