Data Mining - Fordham University
... acted upon. However, even once the mined knowledge is acted upon the data mining process may not be complete and have to be repeated, since the data distribution may change over time, new data may become available, or new evaluation criteria may be introduced. ...
... acted upon. However, even once the mined knowledge is acted upon the data mining process may not be complete and have to be repeated, since the data distribution may change over time, new data may become available, or new evaluation criteria may be introduced. ...
data mining for a web-based educational system
... successfully improved the accuracy of the combined classifier performance by another 10-12%. Such classification is the first step towards a “recommendation system” that will provide valuable, individualized feedback to students. Second, this project extends previous theoretical work regarding clus ...
... successfully improved the accuracy of the combined classifier performance by another 10-12%. Such classification is the first step towards a “recommendation system” that will provide valuable, individualized feedback to students. Second, this project extends previous theoretical work regarding clus ...
Discovery of Meaningful Rules in Time Series
... of a rule is measured with a score called the J-measure. The method was used in several papers before it was shown that the Jmeasure gave the same significance to rules found in completely random data as to rules found in real data [12]. Later analyses by more than a dozen follow-up papers suggest t ...
... of a rule is measured with a score called the J-measure. The method was used in several papers before it was shown that the Jmeasure gave the same significance to rules found in completely random data as to rules found in real data [12]. Later analyses by more than a dozen follow-up papers suggest t ...
A Survey Paper on Data mining Techniques and Challenges in
... practical systems [49]. The technology is successful not by providing accuracy, but by assisting the radiologists and patients. Their team applied SVM initially and then moved to boosting algorithm and neural network. Since SVM is not specific to data domain and the key data characteristics are more ...
... practical systems [49]. The technology is successful not by providing accuracy, but by assisting the radiologists and patients. Their team applied SVM initially and then moved to boosting algorithm and neural network. Since SVM is not specific to data domain and the key data characteristics are more ...
5.3 Quantitative Association Rules
... itemsets in a transaction database [Agrawal1993]. It focused on the enhancement of databases with necessary functionality to process decision support queries. This algorithm was targeted to discover qualitative rules. This technique is limited to only one item in the consequent. That is, the associa ...
... itemsets in a transaction database [Agrawal1993]. It focused on the enhancement of databases with necessary functionality to process decision support queries. This algorithm was targeted to discover qualitative rules. This technique is limited to only one item in the consequent. That is, the associa ...
FROM DATA MINING TO SENTIMENT ANALYSIS Classifying documents through existing opinion mining methods
... This thesis proposes a solution for document-level opinion mining, a method of finding overall opinion from given sources, for example, product reviews, news articles and blogs. This suggestion was done by using existing methods and an unsupervised self-organizing map for classification. The task is ...
... This thesis proposes a solution for document-level opinion mining, a method of finding overall opinion from given sources, for example, product reviews, news articles and blogs. This suggestion was done by using existing methods and an unsupervised self-organizing map for classification. The task is ...
Inducing Decision Trees with an Ant Colony Optimization Algorithm
... leaf node, moving down the tree by selecting branches according to the outcome of attribute tests represented by internal nodes until a leaf node is reached. At this point, the class label associated with the leaf node is the class label predicted for the example. A common approach to create decisio ...
... leaf node, moving down the tree by selecting branches according to the outcome of attribute tests represented by internal nodes until a leaf node is reached. At this point, the class label associated with the leaf node is the class label predicted for the example. A common approach to create decisio ...
Discovering Lag Intervals for Temporal Dependencies
... • Investigates the relationship among the lag intervals and other existing temporal patterns proposed in previous work. It shows that, many existing temporal patterns can be expressed as special cases of temporal dependencies with lag intervals. • Develops an algorithm for discovering appropriate la ...
... • Investigates the relationship among the lag intervals and other existing temporal patterns proposed in previous work. It shows that, many existing temporal patterns can be expressed as special cases of temporal dependencies with lag intervals. • Develops an algorithm for discovering appropriate la ...
Inducing Decision Trees with an Ant Colony Optimization Algorithm
... leaf node, moving down the tree by selecting branches according to the outcome of attribute tests represented by internal nodes until a leaf node is reached. At this point, the class label associated with the leaf node is the class label predicted for the example. A common approach to create decisio ...
... leaf node, moving down the tree by selecting branches according to the outcome of attribute tests represented by internal nodes until a leaf node is reached. At this point, the class label associated with the leaf node is the class label predicted for the example. A common approach to create decisio ...
Improving the Accuracy of Decision Tree Induction by - IBaI
... According to the quality criteria (Nadler and Smyth, 1993) for feature selection, the model for feature selection can be distinguished into the filter model and the wrapper model (Cover, 1977), (Kohavi and John, 1998). The wrapper model attempts to identify the best feature subset for use with a par ...
... According to the quality criteria (Nadler and Smyth, 1993) for feature selection, the model for feature selection can be distinguished into the filter model and the wrapper model (Cover, 1977), (Kohavi and John, 1998). The wrapper model attempts to identify the best feature subset for use with a par ...
A fuzzy decision tree approach to start a genetic
... Table 2 shows the average performances from decisions trees induced by C4.5 and the fuzzy ones for the studied problems. In terms of amount of rules/leaves, it was already expected that the fuzzy trees would be the smallest due to the low induction threshold. The same reason may be used to justify t ...
... Table 2 shows the average performances from decisions trees induced by C4.5 and the fuzzy ones for the studied problems. In terms of amount of rules/leaves, it was already expected that the fuzzy trees would be the smallest due to the low induction threshold. The same reason may be used to justify t ...
Data Mining Methods for Knowledge Discovery in Multi
... existing methods in the following section. 1.1. Limitations of Existing Data Mining Methods for Knowledge Discovery The survey in Part A concludes that while several data mining methods already exist for numerical data, most of them are not tailored to handle MOO datasets, which come with inherent p ...
... existing methods in the following section. 1.1. Limitations of Existing Data Mining Methods for Knowledge Discovery The survey in Part A concludes that while several data mining methods already exist for numerical data, most of them are not tailored to handle MOO datasets, which come with inherent p ...
A privacy-preserving technique for Euclidean distance
... methods is generally suited to just one algorithm and/or scenario as will be illustrated in Sect. 2. There is thus a lack of attempt to have one single integrated method for at least even a collection of algorithms and scenarios. For the random perturbation-based algorithms, the original data distri ...
... methods is generally suited to just one algorithm and/or scenario as will be illustrated in Sect. 2. There is thus a lack of attempt to have one single integrated method for at least even a collection of algorithms and scenarios. For the random perturbation-based algorithms, the original data distri ...
Relationship between Product Based Loyalty
... scalable with stable clustering quality. The clustering must inspect all data points and globally measure their distance from each cluster no matter how close or far away they are. For large data sets the runtime of such an algorithm is intolerably long (Chen, et al., 1996). In machine learning, clu ...
... scalable with stable clustering quality. The clustering must inspect all data points and globally measure their distance from each cluster no matter how close or far away they are. For large data sets the runtime of such an algorithm is intolerably long (Chen, et al., 1996). In machine learning, clu ...
Efficient Frequent Pattern Mining Using Auto
... level- wise algorithm where it first process frequent 1-itemsets then frequent 2-itemsets and so on till maximum frequent n-itemsets. Another characteristic of this algorithm is generateand-test for finding frequent patterns. It requires multiple database scans equal to maximum length of frequent pa ...
... level- wise algorithm where it first process frequent 1-itemsets then frequent 2-itemsets and so on till maximum frequent n-itemsets. Another characteristic of this algorithm is generateand-test for finding frequent patterns. It requires multiple database scans equal to maximum length of frequent pa ...
A Review on Ensembles for the Class Imbalance Problem: Bagging
... decomposition); however, in classification, the concept of diversity is still formally ill-defined [35]. Even though, diversity is necessary [36]–[38] and there exist several different ways to achieve it [39]. In this paper, we focus on data variation-based ensembles, which consist in the manipulati ...
... decomposition); however, in classification, the concept of diversity is still formally ill-defined [35]. Even though, diversity is necessary [36]–[38] and there exist several different ways to achieve it [39]. In this paper, we focus on data variation-based ensembles, which consist in the manipulati ...
Efficient Classification and Prediction Algorithms for Biomedical
... Such patterns may help us understand the process in the future, or we can use those patterns to make predictions: Assuming that the future, at least the near future, will not be much different from the past when the sample data was collected, the future predictions can also be expected to be correct ...
... Such patterns may help us understand the process in the future, or we can use those patterns to make predictions: Assuming that the future, at least the near future, will not be much different from the past when the sample data was collected, the future predictions can also be expected to be correct ...