Data Discretization: Taxonomy and Big Data Challenge
... task and is autonomous from the learning algorithm (23), as a data preprocessing algorithm (71). Almost all isolated known discretizers are static. By contrast, a dynamic discretizer responds when the learner requires so, during the building of the model. Hence, they must belong to the local discret ...
... task and is autonomous from the learning algorithm (23), as a data preprocessing algorithm (71). Almost all isolated known discretizers are static. By contrast, a dynamic discretizer responds when the learner requires so, during the building of the model. Hence, they must belong to the local discret ...
Which Space Partitioning Tree to Use for Search?
... Nearest-neighbor search is ubiquitous in computer science. Several techniques exist for nearestneighbor search, but most algorithms can be categorized into two following groups based on the indexing scheme used – (1) search with hierarchical tree indices, or (2) search with hash-based indices. Altho ...
... Nearest-neighbor search is ubiquitous in computer science. Several techniques exist for nearestneighbor search, but most algorithms can be categorized into two following groups based on the indexing scheme used – (1) search with hierarchical tree indices, or (2) search with hash-based indices. Altho ...
as a PDF
... algorithms (IDE3, CART and C4.5) are based Hunt’s method for tree construction (Srivastava et al, 1998). In Hunt’s algorithm for decision tree construction, training data set is recursively partitioned using depth-first greedy technique, till all the record data sets belong to the class label (Hunts ...
... algorithms (IDE3, CART and C4.5) are based Hunt’s method for tree construction (Srivastava et al, 1998). In Hunt’s algorithm for decision tree construction, training data set is recursively partitioned using depth-first greedy technique, till all the record data sets belong to the class label (Hunts ...
Time series feature extraction for data mining using
... were rules based on only a few time points, it would be unclear why these particular points have been selected. Any time warping effects in new, unclassified data would make the rules useless, because some phenomena might not occur at these exact time locations anymore. Clustering algorithms rely on ...
... were rules based on only a few time points, it would be unclear why these particular points have been selected. Any time warping effects in new, unclassified data would make the rules useless, because some phenomena might not occur at these exact time locations anymore. Clustering algorithms rely on ...
Mining Common Outliers for Intrusion Detection
... anomalies occuring on two different systems, are highly probable to be attacks. Let us detail the short illustration given in section 1 with Ax , an anomaly that is not an attack on site S1 . Ax is probably a context based anomaly, such as a new kind of usage specific to S1 . Therefore, Ax will not ...
... anomalies occuring on two different systems, are highly probable to be attacks. Let us detail the short illustration given in section 1 with Ax , an anomaly that is not an attack on site S1 . Ax is probably a context based anomaly, such as a new kind of usage specific to S1 . Therefore, Ax will not ...
A Clustering based Intrusion Detection System for Storage Area
... used distributed rules for intrusion detection. The effectiveness of IDS depends on the rules selected to detect intrusions. In [7], two IDS approaches have been proposed. The first approach is a real time IDS in which each block that is being transmitted from or to SAN is evaluated in real time for ...
... used distributed rules for intrusion detection. The effectiveness of IDS depends on the rules selected to detect intrusions. In [7], two IDS approaches have been proposed. The first approach is a real time IDS in which each block that is being transmitted from or to SAN is evaluated in real time for ...
Answers to Exercises
... manufactured (day of week), road conditions at the time of the accident etc. An argument for database query can also be made. ...
... manufactured (day of week), road conditions at the time of the accident etc. An argument for database query can also be made. ...
Frequent Closures as a Concise Representation for Binary Data
... sets can be computed in real-life large datasets thanks to the support threshold on one hand and safe pruning criteria that drastically reduces the search space on the other hand (e.g., the so-called apriori trick [2]). However, there is still an active research on algorithms, not only for the frequ ...
... sets can be computed in real-life large datasets thanks to the support threshold on one hand and safe pruning criteria that drastically reduces the search space on the other hand (e.g., the so-called apriori trick [2]). However, there is still an active research on algorithms, not only for the frequ ...
Ensembles of data-reduction-based classifiers for distributed
... the size of the original data set (about 80-100%), being their sum greater than the total amount. Even assuming an ideal multiprocessing configuration, Bagging could yield a negligible (or zero) reduction of the total effort, which makes this technique not suitable for direct managing large data set ...
... the size of the original data set (about 80-100%), being their sum greater than the total amount. Even assuming an ideal multiprocessing configuration, Bagging could yield a negligible (or zero) reduction of the total effort, which makes this technique not suitable for direct managing large data set ...
Feature Selection for Multi-Label Learning
... so, two steps are required: (1) Selection and (2) Generation. The former chooses pairs of labels, whereas the latter combines the labels within each pair to generate a new label. The variables constructed are then included as new labels in the original dataset and the standard multi-label FS approac ...
... so, two steps are required: (1) Selection and (2) Generation. The former chooses pairs of labels, whereas the latter combines the labels within each pair to generate a new label. The variables constructed are then included as new labels in the original dataset and the standard multi-label FS approac ...
Data Mining in Computational Biology
... objects may be thrown away as noise, or they may be the “interesting” ones, depending on the specific application scenario. For example, given microarray data, we might be able to find a tissue sample that is unlike any other seen, or we might be able to identify genes with expression levels very di ...
... objects may be thrown away as noise, or they may be the “interesting” ones, depending on the specific application scenario. For example, given microarray data, we might be able to find a tissue sample that is unlike any other seen, or we might be able to identify genes with expression levels very di ...
The PDF of the Chapter - A Programmer`s Guide to Data Mining
... basketball players, one-third of the entries in each bucket should also be basketball players. And one-third the entries should be gymnasts and one-third marathoners. This is called stratification and this is a good thing. The problem with the leave-one-out evaluation method is that necessarily all ...
... basketball players, one-third of the entries in each bucket should also be basketball players. And one-third the entries should be gymnasts and one-third marathoners. This is called stratification and this is a good thing. The problem with the leave-one-out evaluation method is that necessarily all ...
