Mining Spatio-Temporal Association Rules
... patterns describe many mobility characteristics and can be used to predict future movements. We take the approach of mining our patterns on a time window by time window basis. We think this is important because it allows us to see the changing nature of the patterns over time, and allows for interac ...
... patterns describe many mobility characteristics and can be used to predict future movements. We take the approach of mining our patterns on a time window by time window basis. We think this is important because it allows us to see the changing nature of the patterns over time, and allows for interac ...
Discovering New Rule Induction Algorithms with Grammar
... in the training set in which the value of salary is greater than £100,000, regardless of the current value of the class attribute of an example. The learning process goes on until a pre-defined criterion is satisfied. This criterion usually requires that all or almost all examples in the training se ...
... in the training set in which the value of salary is greater than £100,000, regardless of the current value of the class attribute of an example. The learning process goes on until a pre-defined criterion is satisfied. This criterion usually requires that all or almost all examples in the training se ...
PPT - Computer Science Department
... The key principle for effective sampling is the following: – using a sample will work almost as well as using the entire data sets, if the sample is representative – A sample is representative if it has approximately the same property (of interest) as the original set of data ...
... The key principle for effective sampling is the following: – using a sample will work almost as well as using the entire data sets, if the sample is representative – A sample is representative if it has approximately the same property (of interest) as the original set of data ...
Anomaly Detection and Preprocessing
... is defined as an observation that deviates too much from other observations that it arouses suspicions that it was generated by a different mechanism from other observations” [28]. Anomaly detection is an inherently difficult problem as it is essentially the problem of deciding what is not normal; f ...
... is defined as an observation that deviates too much from other observations that it arouses suspicions that it was generated by a different mechanism from other observations” [28]. Anomaly detection is an inherently difficult problem as it is essentially the problem of deciding what is not normal; f ...
One Click Mining - Polo Club of Data Science
... fetching its result patterns Pl . All mining results are then stored in a pattern cache, for which we denote by Cl ⊆ P the state before the results of mining round l are added. The cache has a finite cache capacity c ∈ N such that at all times l it is enforced that |Cl | ≤ c. Finally, the performanc ...
... fetching its result patterns Pl . All mining results are then stored in a pattern cache, for which we denote by Cl ⊆ P the state before the results of mining round l are added. The cache has a finite cache capacity c ∈ N such that at all times l it is enforced that |Cl | ≤ c. Finally, the performanc ...
data warehousing and data mining
... Traditional database is transaction-oriented while data warehouse is data-retrieval optimized for decision-support Data Warehouse "A subject-oriented, integrated, time-variant, and non-volatile collection of data in support of management's decision-making process" OLAP (on-line analytical proc ...
... Traditional database is transaction-oriented while data warehouse is data-retrieval optimized for decision-support Data Warehouse "A subject-oriented, integrated, time-variant, and non-volatile collection of data in support of management's decision-making process" OLAP (on-line analytical proc ...
Association Discovery in Two-View Data
... evoked emotions. In this case it would be of interest to investigate which emotions are evoked by which types of music: how are the music features associated to emotions? Example patterns our method finds are, e.g., that R&B songs are typically catchy and associated with positive feelings, that alte ...
... evoked emotions. In this case it would be of interest to investigate which emotions are evoked by which types of music: how are the music features associated to emotions? Example patterns our method finds are, e.g., that R&B songs are typically catchy and associated with positive feelings, that alte ...
Teaching Data Mining in the Era of Big Data
... knowledge that can potentially improve our understanding of the world around us. The challenge before us lies in the development of systems and methods that can extract these nuggets. We are in a new era in modern information technology - the “Big Data” era. In March, 2012, the U.S. Government anno ...
... knowledge that can potentially improve our understanding of the world around us. The challenge before us lies in the development of systems and methods that can extract these nuggets. We are in a new era in modern information technology - the “Big Data” era. In March, 2012, the U.S. Government anno ...
DSS Chapter 1 - (Walid) Ben Ali
... Statistical methods (including both hierarchical and nonhierarchical), such as k-means, k-modes, and so on Neural networks (adaptive resonance theory [ART], self-organizing map [SOM]) Fuzzy logic (e.g., fuzzy c-means algorithm) Genetic algorithms ...
... Statistical methods (including both hierarchical and nonhierarchical), such as k-means, k-modes, and so on Neural networks (adaptive resonance theory [ART], self-organizing map [SOM]) Fuzzy logic (e.g., fuzzy c-means algorithm) Genetic algorithms ...
Orange4WS Environment for Service
... KNIME [4], RapidMiner [5] and Orange [2]), the Orange4WS platform provides the following new functionalities: (a) userfriendly composition of data mining workflows from local and distributed data processing/mining algorithms applied to a combination of local and distributed data/knowledge sources, ( ...
... KNIME [4], RapidMiner [5] and Orange [2]), the Orange4WS platform provides the following new functionalities: (a) userfriendly composition of data mining workflows from local and distributed data processing/mining algorithms applied to a combination of local and distributed data/knowledge sources, ( ...
Extracting Temporal Patterns from Interval-Based Sequences
... intervals associated to events in patterns. Experiments on simulated data show that our algorithm is efficient for extracting precise patterns even in noisy contexts and that it improves the performance of a former algorithm which used a clustering method based on the EM algorithm. ...
... intervals associated to events in patterns. Experiments on simulated data show that our algorithm is efficient for extracting precise patterns even in noisy contexts and that it improves the performance of a former algorithm which used a clustering method based on the EM algorithm. ...
Scaling up classification rule induction through parallel processing
... ID3 decision tree induction algorithm (Quinlan, 1983). Windowing initially takes a random sample, the window, from the data set. The initial size of the window is specified by the user. The window is used to induce a classifier. The induced classifier is then applied to the remaining instances. Inst ...
... ID3 decision tree induction algorithm (Quinlan, 1983). Windowing initially takes a random sample, the window, from the data set. The initial size of the window is specified by the user. The window is used to induce a classifier. The induced classifier is then applied to the remaining instances. Inst ...
data - Université Nice Sophia Antipolis
... – smooth by fitting the data into regression functions • Clustering – detect and remove outliers • Combined computer and human inspection – detect suspicious values and check by human (e.g., deal with possible outliers) Andrea G. B. Tettamanzi, 2016 ...
... – smooth by fitting the data into regression functions • Clustering – detect and remove outliers • Combined computer and human inspection – detect suspicious values and check by human (e.g., deal with possible outliers) Andrea G. B. Tettamanzi, 2016 ...
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.