Analysis of Various Periodicity Detection Algorithms in Time Series
... with the closed patterns to produce a set of rules called representative rules for forward, backward in-between temporal conditions among events in one general representation. Avrilia Floratou et al [13] give a technique for efficient and accurate discovery of patterns in sequence datasets. The main ...
... with the closed patterns to produce a set of rules called representative rules for forward, backward in-between temporal conditions among events in one general representation. Avrilia Floratou et al [13] give a technique for efficient and accurate discovery of patterns in sequence datasets. The main ...
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... new and useful information from the data already taken and archived is becoming a major endeavor in itself. This action of knowledge discovery in databases (KDD), is what is most commonly inferred by the phrase data mining, and it forms the basis for our review. Astronomy has been among the first sc ...
... new and useful information from the data already taken and archived is becoming a major endeavor in itself. This action of knowledge discovery in databases (KDD), is what is most commonly inferred by the phrase data mining, and it forms the basis for our review. Astronomy has been among the first sc ...
Efficient Mining of Frequent Itemsets on Large Uncertain Databases
... Mining evolving databases. We also study the important problem of maintaining mining results for changing, or evolving, databases. The type of evolving data that we address here is about the appending, or insertion of a batch of tuples to the database. Tuple insertion is common in the applications t ...
... Mining evolving databases. We also study the important problem of maintaining mining results for changing, or evolving, databases. The type of evolving data that we address here is about the appending, or insertion of a batch of tuples to the database. Tuple insertion is common in the applications t ...
Stream Data Mining
... Data mining process is a step in Knowledge Discovery Process consisting of methods that produce useful patterns or models from the data [3]. In some cases when the problem is known, correct data is available as well, and there is an attempts to find the models or tools which will be used, some probl ...
... Data mining process is a step in Knowledge Discovery Process consisting of methods that produce useful patterns or models from the data [3]. In some cases when the problem is known, correct data is available as well, and there is an attempts to find the models or tools which will be used, some probl ...
Modeling, Storing and Mining Moving Object Databases
... To capture a ‘trajectory’, we need an identification of the mobile device (indicated by ‘object id’), the actual trajectory (‘trajectory id’) as well as the position of the trajectory itself. In other words, ‘position’ describes the trace of the moving vehicle. The data types used are abstract, sinc ...
... To capture a ‘trajectory’, we need an identification of the mobile device (indicated by ‘object id’), the actual trajectory (‘trajectory id’) as well as the position of the trajectory itself. In other words, ‘position’ describes the trace of the moving vehicle. The data types used are abstract, sinc ...
An Analytical Study on Sequential Pattern Mining With Progressive
... sequential pattern-mining techniques in the literature. This paper classifying sequential pattern-mining algorithms based on important key features supported by the techniques. This classification aims at understanding of sequential patternmining problems, current status of provided solutions, and d ...
... sequential pattern-mining techniques in the literature. This paper classifying sequential pattern-mining algorithms based on important key features supported by the techniques. This classification aims at understanding of sequential patternmining problems, current status of provided solutions, and d ...
University of Alberta Library Release Form Name of Author Title of Thesis
... be neighbors of p. |D(q, p) − D(p, oi )| is close to D(q, p) and can be larger than the current k-nn distance upper bound so that oi will be pruned away. . 92 5.2 The average numbers of distance computations per query by all seven k-nn search methods on HIV-1 dataset, for k = 1, 3, 5, 10. . . . . . ...
... be neighbors of p. |D(q, p) − D(p, oi )| is close to D(q, p) and can be larger than the current k-nn distance upper bound so that oi will be pruned away. . 92 5.2 The average numbers of distance computations per query by all seven k-nn search methods on HIV-1 dataset, for k = 1, 3, 5, 10. . . . . . ...
Improving Accuracy of Classification Models Induced from
... need for manually producing domain hierarchy trees. Their method, called k-Anonymity of Classification Trees Using Suppression (kACTUS), identifies attributes that have less influence on the classification of the data records; those attributes are then suppressed until the table becomes k-anonymized ...
... need for manually producing domain hierarchy trees. Their method, called k-Anonymity of Classification Trees Using Suppression (kACTUS), identifies attributes that have less influence on the classification of the data records; those attributes are then suppressed until the table becomes k-anonymized ...
Data Mining: An Overview from Database Perspective
... to nd di erent association patterns, the amount of processing could be huge, and performance improvement is an essential concern at mining such rules. Ecient algorithms for mining association rules and some methods for further performance enhancements will be examined in Section 3. The most popula ...
... to nd di erent association patterns, the amount of processing could be huge, and performance improvement is an essential concern at mining such rules. Ecient algorithms for mining association rules and some methods for further performance enhancements will be examined in Section 3. The most popula ...
Automatically Detecting Avalanche Events in Passive Seismic
... that the fuzzy logic rules are based on manual analysis of previous data; in other words, the rules are not derived automatically or in a timely manner. The authors of [10] acknowledge this drawback, and even state that users must modify the rules to adapt to other sites; however, modifying rules fo ...
... that the fuzzy logic rules are based on manual analysis of previous data; in other words, the rules are not derived automatically or in a timely manner. The authors of [10] acknowledge this drawback, and even state that users must modify the rules to adapt to other sites; however, modifying rules fo ...
A New Sequential Covering Strategy for Inducing Classification
... ATA mining is a research area concentrated on designing and employing computational methods to discover (learn) a model (based on a given knowledge representation) from real-world structured data [1], [2]. Most of research is ...
... ATA mining is a research area concentrated on designing and employing computational methods to discover (learn) a model (based on a given knowledge representation) from real-world structured data [1], [2]. Most of research is ...
Mining and Using Sets of Patterns through Compression
... The primary cause is the pattern explosion. While strict constraints only result in few patterns, these are seldom informative: they are the most obvious patterns, and hence often long-since common knowledge. However, when we loosen the constraints—to discover novel associations—the pattern explosio ...
... The primary cause is the pattern explosion. While strict constraints only result in few patterns, these are seldom informative: they are the most obvious patterns, and hence often long-since common knowledge. However, when we loosen the constraints—to discover novel associations—the pattern explosio ...
Full Text
... Data mining process is a step in Knowledge Discovery Process consisting of methods that produce useful patterns or models from the data [3]. In some cases when the problem is known, correct data is available as well, and there is an attempts to find the models or tools which will be used, some probl ...
... Data mining process is a step in Knowledge Discovery Process consisting of methods that produce useful patterns or models from the data [3]. In some cases when the problem is known, correct data is available as well, and there is an attempts to find the models or tools which will be used, some probl ...
A Fast Algorithm For Data Mining
... itemsets in large data repositories. Frequent itemsets correspond to the set of items that occur frequently in transactions in a database. Several novel algorithms have been developed recently to mine closed frequent itemsets these itemsets are a subset of the frequent itemsets. These algorithms are ...
... itemsets in large data repositories. Frequent itemsets correspond to the set of items that occur frequently in transactions in a database. Several novel algorithms have been developed recently to mine closed frequent itemsets these itemsets are a subset of the frequent itemsets. These algorithms are ...
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.