Precision-recall space to correct external indices for biclustering
... bias. Our approach is to apply the following correction: ...
... bias. Our approach is to apply the following correction: ...
Pruning and Grouping Discovered Association Rules 1 Introduction
... Given an attribute set Y , the cover for the set of rules of the form X ) Y can still be quite large. The set of rules in the cover can be made more understandable by ordering and grouping the rules. Rules can be ordered based on their interestingness. Obvious measures of interestingness are the con ...
... Given an attribute set Y , the cover for the set of rules of the form X ) Y can still be quite large. The set of rules in the cover can be made more understandable by ordering and grouping the rules. Rules can be ordered based on their interestingness. Obvious measures of interestingness are the con ...
Technologies and Computational Intelligence
... What is Big Data? No single standard definition Big data is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. “Big Data” is data whose scale, diversity, and complexity require new ...
... What is Big Data? No single standard definition Big data is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. “Big Data” is data whose scale, diversity, and complexity require new ...
Intelligent Miner for Data Applications Guide
... Associations for Good Customer Set: LIS Removed Associations for Good Customer Set: LIS Removed, Detail . . Associations on Okay Customer Set . . . . . . . . . . . . . . . . Associations on Okay Customer Set Detail . . . . . . . . . . . . ...
... Associations for Good Customer Set: LIS Removed Associations for Good Customer Set: LIS Removed, Detail . . Associations on Okay Customer Set . . . . . . . . . . . . . . . . Associations on Okay Customer Set Detail . . . . . . . . . . . . ...
Querying and Mining of Time Series Data
... we used L1 (Manhattan), L2 (Euclidean) and L∞ (Maximum) norms (c.f. [43]). In the sequel, the terms Euclidean distance and L2 norm will be used interchangeably. Besides being relatively straightforward and intuitive, Euclidean distance and its variants have several other advantages. The complexity o ...
... we used L1 (Manhattan), L2 (Euclidean) and L∞ (Maximum) norms (c.f. [43]). In the sequel, the terms Euclidean distance and L2 norm will be used interchangeably. Besides being relatively straightforward and intuitive, Euclidean distance and its variants have several other advantages. The complexity o ...
Service-Oriented Data Mining
... The SOMiner architecture is depicted in the diagram of Fig. 1. It is an execution environment that is designed and implemented according to a multi-layer structure. All interaction during the processing of a user request happens over the Web, based on a user interface that controls access to the ind ...
... The SOMiner architecture is depicted in the diagram of Fig. 1. It is an execution environment that is designed and implemented according to a multi-layer structure. All interaction during the processing of a user request happens over the Web, based on a user interface that controls access to the ind ...
Peculiarity oriented multidatabase mining
... patterns [4]. Their method is essentially a study of the change of supports in different data sets. A large change suggests an interesting emerging pattern. Since emerging patterns with large supports are perhaps well-known facts, they concentrated on emerging patterns with small supports. In partic ...
... patterns [4]. Their method is essentially a study of the change of supports in different data sets. A large change suggests an interesting emerging pattern. Since emerging patterns with large supports are perhaps well-known facts, they concentrated on emerging patterns with small supports. In partic ...
A REVIEW ON SPATIAL DATA MINING METHODS AND
... A spatial database is the database which has been specially optimized to store data pertaining to objects in the real world. In other words spatial data is the data which represents objects in geometric space. The objects are stored in database in the form of lines, points and polygons. A Relational ...
... A spatial database is the database which has been specially optimized to store data pertaining to objects in the real world. In other words spatial data is the data which represents objects in geometric space. The objects are stored in database in the form of lines, points and polygons. A Relational ...
2082-4599-1-SP - Majlesi Journal of Electrical Engineering
... ISL algorithm: this algorithm is similar to DSR algorithm with the difference that it chooses the transactions which do not support sensitive rule and adds sensitive LHS to them and if there is not any transaction and the amount of confidence is not still less than threshold, the rule will not be hi ...
... ISL algorithm: this algorithm is similar to DSR algorithm with the difference that it chooses the transactions which do not support sensitive rule and adds sensitive LHS to them and if there is not any transaction and the amount of confidence is not still less than threshold, the rule will not be hi ...
PG Academic Handbook
... These courses will usually cover topics that are not generally covered in the regular courses. Interested students can register for these courses for credits, provided, the above semester-wise credit structure is followed. They are evaluated like any other courses and credits earned count towards de ...
... These courses will usually cover topics that are not generally covered in the regular courses. Interested students can register for these courses for credits, provided, the above semester-wise credit structure is followed. They are evaluated like any other courses and credits earned count towards de ...
CRISP-DM: A Standard Process Model for Data Mining
... The following statement creates a data mining model to find out those products which sell together based on an association algorithm. The model is interested only in rules with at least five items: Create Mining Model MyAssociationModel ( Transaction_id long key, [Product purchases] table predict ( ...
... The following statement creates a data mining model to find out those products which sell together based on an association algorithm. The model is interested only in rules with at least five items: Create Mining Model MyAssociationModel ( Transaction_id long key, [Product purchases] table predict ( ...
Design Patterns
... Discuss in detail about the classification of design patterns. What is delegation? Explain. List and explain the reusable object oriented design aspects of a pattern. Draw and explain the GUI factor class hierarchy. Difference between compositor and composition. Explain in detail about the recursive ...
... Discuss in detail about the classification of design patterns. What is delegation? Explain. List and explain the reusable object oriented design aspects of a pattern. Draw and explain the GUI factor class hierarchy. Difference between compositor and composition. Explain in detail about the recursive ...
Data Mining Applications in Big Data
... way to obtain useful knowledge. Data stream can be from sensor networks, measurements in network monitoring and traffic management, click-streams in web exploring, manufacturing processes, and twitter posts, etc. [4]. Data stream mining studies methods and algorithms for extracting knowledge from vo ...
... way to obtain useful knowledge. Data stream can be from sensor networks, measurements in network monitoring and traffic management, click-streams in web exploring, manufacturing processes, and twitter posts, etc. [4]. Data stream mining studies methods and algorithms for extracting knowledge from vo ...
Indexing Density Models for Incremental Learning and Anytime
... data. Any model assumption (e.g. a single multivariate normal distribution) may not reflect the true distribution. Mixture densities relax this assumption that the data follows exactly one unimodal model by assuming that the data follows a combination of probability density functions. In our work we ...
... data. Any model assumption (e.g. a single multivariate normal distribution) may not reflect the true distribution. Mixture densities relax this assumption that the data follows exactly one unimodal model by assuming that the data follows a combination of probability density functions. In our work we ...
Knowledge discovery from text and links
... evaluation. Step 4 (machine learning): Bayesian modeling, using word/n-gram frequency. ...
... evaluation. Step 4 (machine learning): Bayesian modeling, using word/n-gram frequency. ...
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 ...
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.