Data Mining
... E.g., classify people as healty or sick, or classify transactions as fraudulent or not ...
... E.g., classify people as healty or sick, or classify transactions as fraudulent or not ...
Chapter2 - Department of Computer Science
... • Possible with any finite set of finite relations • Problematic: relationships without a pre-specified number of ...
... • Possible with any finite set of finite relations • Problematic: relationships without a pre-specified number of ...
Data Mining Ethics in Privacy Preservation - A Survey
... Oliveira and Zaiane also in [5] aims at balancing between privacy and disclosure of information by trying to minimize the impact on sanitized transactions or else to minimize the accidentally hidden and ghost rules. Wang et al. propose a matrix based sanitization approach to hide the sensitive patte ...
... Oliveira and Zaiane also in [5] aims at balancing between privacy and disclosure of information by trying to minimize the impact on sanitized transactions or else to minimize the accidentally hidden and ghost rules. Wang et al. propose a matrix based sanitization approach to hide the sensitive patte ...
Data Mining - Fordham University
... just described, we could evaluate the decision tree based on its accuracy—the percentage of its predictions that are correct. However, many other evaluation or performance metrics are possible and for this specific example return on investment might actually be a better metric. The data mining proce ...
... just described, we could evaluate the decision tree based on its accuracy—the percentage of its predictions that are correct. However, many other evaluation or performance metrics are possible and for this specific example return on investment might actually be a better metric. The data mining proce ...
Slides for COP5992 - Florida International University
... survey images (from Palomar Observatory). – 3000 images with 23,040 x 23,040 pixels per image. ...
... survey images (from Palomar Observatory). – 3000 images with 23,040 x 23,040 pixels per image. ...
A Combined Approach for Segment-Specific Analysis of
... To overcome the limitations inherent to both exploratory attempts (lack of implications for managerial decision making) and explanatory models (issue of proper category selection and computational restrictions) for analyzing market basket data, we next introduce a procedure that combines the specifi ...
... To overcome the limitations inherent to both exploratory attempts (lack of implications for managerial decision making) and explanatory models (issue of proper category selection and computational restrictions) for analyzing market basket data, we next introduce a procedure that combines the specifi ...
A Comparative Study of Frequent and Maximal Periodic Pattern
... Data mining refers to extracting or “mining” knowledge from large amounts of data. The term is actually a misnomer. Remember that the mining of gold from rocks or sand is referred to as gold mining rather than rock or sand mining. Thus, data mining should have been more appropriately named “knowledg ...
... Data mining refers to extracting or “mining” knowledge from large amounts of data. The term is actually a misnomer. Remember that the mining of gold from rocks or sand is referred to as gold mining rather than rock or sand mining. Thus, data mining should have been more appropriately named “knowledg ...
Application of data mining in a maintenance system for failure
... degradation. A numerical example is given where the computational results show that the integrated scheduling model has better performance than the existing models, which proves its efficiency. Raheja et al. 2006 present a work proposing a combined data fusion/data mining-based architecture for Cond ...
... degradation. A numerical example is given where the computational results show that the integrated scheduling model has better performance than the existing models, which proves its efficiency. Raheja et al. 2006 present a work proposing a combined data fusion/data mining-based architecture for Cond ...
Large Graph Data Mining and Data Warehousing
... Probably the most important topic studied in graph mining Graph area: referred as community detection ...
... Probably the most important topic studied in graph mining Graph area: referred as community detection ...
Outlier detection in spatial data using the m
... has been done in outlier detection and these are classified into different types with respect to the detection approach being used. Exemplar techniques include Classification based methods, Nearest Neighbor based methods, Cluster based methods and Statistical based ...
... has been done in outlier detection and these are classified into different types with respect to the detection approach being used. Exemplar techniques include Classification based methods, Nearest Neighbor based methods, Cluster based methods and Statistical based ...
SENTIMENT ANALYSIS USING SVM AND NAÏVE BAYES
... The above algorithm can be represented using figure 2 ...
... The above algorithm can be represented using figure 2 ...
Chapter 8 INTRODUCTION TO SUPERVISED METHODS
... with probability of 1 − δ where ε̂(h, S) represents the training error of classifier h measured on training set S of cardinality m and ε(h, D) represents the generalization error of the classifier h over the distribution D. The VC-dimension is a property of a set of all classifiers, denoted by H, th ...
... with probability of 1 − δ where ε̂(h, S) represents the training error of classifier h measured on training set S of cardinality m and ε(h, D) represents the generalization error of the classifier h over the distribution D. The VC-dimension is a property of a set of all classifiers, denoted by H, th ...
Mining Query Subtopics from Search Log Data
... query in order to clarify its subtopic – The URLs clicked after searching both with the original and the expanded queries tend to represent the same subtopic – The key word tends to be indicative of the subtopic – E.g. • harry shum microsoft ...
... query in order to clarify its subtopic – The URLs clicked after searching both with the original and the expanded queries tend to represent the same subtopic – The key word tends to be indicative of the subtopic – E.g. • harry shum microsoft ...
A Streaming Parallel Decision Tree Algorithm
... classification accuracy. The processors build histograms describing the data they observed and send them to a master processor. Algorithm 6 specifies which histograms are built and how. The number of bins in the histograms is specified through a trade-off between accuracy and computational load: A l ...
... classification accuracy. The processors build histograms describing the data they observed and send them to a master processor. Algorithm 6 specifies which histograms are built and how. The number of bins in the histograms is specified through a trade-off between accuracy and computational load: A l ...
Learning Complexity-Bounded Rule
... is not as easy in many greedy approaches. Just to give an example, the well-known “small disjuncts” problem [7] can easily be avoided. The latter refers to the problem that, in order to be as accurate as possible, many algorithms that successively split the data have to induce relatively “small”, sp ...
... is not as easy in many greedy approaches. Just to give an example, the well-known “small disjuncts” problem [7] can easily be avoided. The latter refers to the problem that, in order to be as accurate as possible, many algorithms that successively split the data have to induce relatively “small”, sp ...
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.