Peculiarity oriented multidatabase mining
... The purpose of data mining is to discover interesting knowledge hidden in databases. The evaluation of interestingness, such as peculiarity, surprisingness, unexpectedness, usefulness, and novelty, can be done in preprocessing and/or postprocessing of the knowledge discovery process [5], [6], [10], ...
... The purpose of data mining is to discover interesting knowledge hidden in databases. The evaluation of interestingness, such as peculiarity, surprisingness, unexpectedness, usefulness, and novelty, can be done in preprocessing and/or postprocessing of the knowledge discovery process [5], [6], [10], ...
Robust Predictive Models on MOOCs
... training a single model on a particular dataset. Although plenty of classification algorithms exist, there is no systematic a-priori method to determine which one is best suited to a particular problem. This is the first hurdle that must be overcome when building a robust model. The second hurdle in ...
... training a single model on a particular dataset. Although plenty of classification algorithms exist, there is no systematic a-priori method to determine which one is best suited to a particular problem. This is the first hurdle that must be overcome when building a robust model. The second hurdle in ...
A Survey of Frequent and Infrequent Weighted Itemset Mining
... Positive and Negative Association rule In[9]X.wuEfficient mining of both positive and negative association rules . They focus on identifying the associations among frequent itemsets. They designed a new method for efficiently mining both positive and negative association rules in databases. This app ...
... Positive and Negative Association rule In[9]X.wuEfficient mining of both positive and negative association rules . They focus on identifying the associations among frequent itemsets. They designed a new method for efficiently mining both positive and negative association rules in databases. This app ...
A General Survey of Privacy-Preserving Data Mining Models and
... it also leads to some weaknesses, since it treats all records equally irrespective of their local density. Therefore, outlier records are more susceptible to adversarial attacks as compared to records in more dense regions in the data [10]. In order to guard against this, one may need to be needless ...
... it also leads to some weaknesses, since it treats all records equally irrespective of their local density. Therefore, outlier records are more susceptible to adversarial attacks as compared to records in more dense regions in the data [10]. In order to guard against this, one may need to be needless ...
Computing the minimum-support for mining frequent patterns
... The first step of the support-confidence framework is to generate frequent itemsets using the Apriori algorithm. In other words, for a given database, the Apriori algorithm generates those itemsets whose supports are greater than, or equal to, a user-specified minimum-support. As have argued previou ...
... The first step of the support-confidence framework is to generate frequent itemsets using the Apriori algorithm. In other words, for a given database, the Apriori algorithm generates those itemsets whose supports are greater than, or equal to, a user-specified minimum-support. As have argued previou ...
Closed Sets for Labeled Data - Journal of Machine Learning Research
... find characteristic rules as combinations of features with high coverage. If there are several rules with the same coverage, most specific rules (with more features) are appropriate for description and explanation purposes. On the other hand, the closely related task of contrast set mining aims at c ...
... find characteristic rules as combinations of features with high coverage. If there are several rules with the same coverage, most specific rules (with more features) are appropriate for description and explanation purposes. On the other hand, the closely related task of contrast set mining aims at c ...
Applying data mining for ontology building
... Data Mining is the process of finding and extracting new and potentially useful knowledge from data. Data mining is also known as Knowledge discovery in databases (KDD). The terms “Data mining” and “Knowledge discovery in database” are used interchangeably [1]. Data mining is an interdisciplinary fi ...
... Data Mining is the process of finding and extracting new and potentially useful knowledge from data. Data mining is also known as Knowledge discovery in databases (KDD). The terms “Data mining” and “Knowledge discovery in database” are used interchangeably [1]. Data mining is an interdisciplinary fi ...
data mining - a domain specific analytical tool for decision
... rule representation can significantly restrict the functional form (and, thus, the approximation power) of the model. 6.8 Nonlinear Regression and Classification Methods: These methods consist of a family of techniques for prediction that fit linear and nonlinear combinations of functions to combina ...
... rule representation can significantly restrict the functional form (and, thus, the approximation power) of the model. 6.8 Nonlinear Regression and Classification Methods: These methods consist of a family of techniques for prediction that fit linear and nonlinear combinations of functions to combina ...
Classification and Decision Trees
... → remove subtrees or branches, in a bottom-up manner, to improve the estimated accuracy on new cases. • conditions for stopping partitioning: • all samples for a given node belong to the same class • there are no remaining attributes for further partitioning • there are no samples left Iza Moise, Ev ...
... → remove subtrees or branches, in a bottom-up manner, to improve the estimated accuracy on new cases. • conditions for stopping partitioning: • all samples for a given node belong to the same class • there are no remaining attributes for further partitioning • there are no samples left Iza Moise, Ev ...
rule induction using probabilistic approximations and data with
... probabilistic approximations, different from lower and upper approximations, are truly better than lower and upper approximations. To accomplish this objective, we conducted experiments of a single ten-fold cross validation increasing the parameter α, with increments equal to 0.1, from 0 to 1.0. For ...
... probabilistic approximations, different from lower and upper approximations, are truly better than lower and upper approximations. To accomplish this objective, we conducted experiments of a single ten-fold cross validation increasing the parameter α, with increments equal to 0.1, from 0 to 1.0. For ...
Spatial Data Mining - KGISL Institute of Information Management
... spatial data. Statistical analysis is a well-studied area and therefore there exist a large number of algorithms including various optimization techniques. It handles very well numerical data and usually comes up with realistic models of spatial phenomena. The major disadvantage of this approach is ...
... spatial data. Statistical analysis is a well-studied area and therefore there exist a large number of algorithms including various optimization techniques. It handles very well numerical data and usually comes up with realistic models of spatial phenomena. The major disadvantage of this approach is ...
Efficient Frequent Pattern Mining Using Auto
... the algorithms are still uncertain. Hence, it is important to continue research in this area in order to find answers to the current challenges. The objective of the proposed work is to implement efficient strategies to traverse and reduce the search space, and to reduce the I/O computations. The pr ...
... the algorithms are still uncertain. Hence, it is important to continue research in this area in order to find answers to the current challenges. The objective of the proposed work is to implement efficient strategies to traverse and reduce the search space, and to reduce the I/O computations. The pr ...
Generic Pattern Mining via Data Mining Template Library
... these different types of patterns; in a generic sense a pattern denotes links/relationships between several objects of interest. The objects are denoted as nodes, and the links as edges. Patterns can have multiple labels, denoting various attributes, on both the nodes and edges. ...
... these different types of patterns; in a generic sense a pattern denotes links/relationships between several objects of interest. The objects are denoted as nodes, and the links as edges. Patterns can have multiple labels, denoting various attributes, on both the nodes and edges. ...
Smoothing Categorical Data
... from the fact that the transactions in D1 and D2 are item sets themselves. The second observation is that D1 and D2 will be indistinguishable by any type of statistical analysis [2]. For, all such analysis boils down to computing aggregates computed on subtables of a dataset. Given that these subtab ...
... from the fact that the transactions in D1 and D2 are item sets themselves. The second observation is that D1 and D2 will be indistinguishable by any type of statistical analysis [2]. For, all such analysis boils down to computing aggregates computed on subtables of a dataset. Given that these subtab ...
EBSCAN: An Entanglement-based Algorithm for Discovering Dense
... cannot be a solution to our geo-social clustering problem. Partitioning and hierarchical clustering are suitable for finding spherical clusters in a spatial database. However, a geographical cluster takes various arbitrary shapes. In contrast with partitioning and hierarchal clustering, density-base ...
... cannot be a solution to our geo-social clustering problem. Partitioning and hierarchical clustering are suitable for finding spherical clusters in a spatial database. However, a geographical cluster takes various arbitrary shapes. In contrast with partitioning and hierarchal clustering, density-base ...
Time Series Data Mining Methods: A Review - EDOC HU
... (IBM, 2014) even labeled as the new natural resource of the century. Considering the various sources of big data in real life, this trend is not surprising. With the vast amount and variety of data available, the capacity of manual data analysis has been exceeded longly. So, the explosion of informa ...
... (IBM, 2014) even labeled as the new natural resource of the century. Considering the various sources of big data in real life, this trend is not surprising. With the vast amount and variety of data available, the capacity of manual data analysis has been exceeded longly. So, the explosion of informa ...
Knowledge Discovery in Spatial Databases
... connected to object via some edge of graph satisfying the conditions expressed by the predicate pred. The additional selection condition pred is used if we want to restrict the investigation explicitly to certain types of neighbors. The definition of the predicate pred may use spatial as well as non ...
... connected to object via some edge of graph satisfying the conditions expressed by the predicate pred. The additional selection condition pred is used if we want to restrict the investigation explicitly to certain types of neighbors. The definition of the predicate pred may use spatial as well as non ...
Discovery of Meaningful Rules in Time Series
... ABSTRACT The ability to make predictions about future events is at the heart of much of science; so, it is not surprising that prediction has been a topic of great interest in the data mining community for the last decade. Most of the previous work has attempted to predict the future based on the cu ...
... ABSTRACT The ability to make predictions about future events is at the heart of much of science; so, it is not surprising that prediction has been a topic of great interest in the data mining community for the last decade. Most of the previous work has attempted to predict the future based on the cu ...
From Data Mining to Knowledge Mining
... Similarly, a numerical taxonomy technique can create a classification of entities, and specify a numerical similarity among the entities assembled into the same or different categories, but it cannot alone build qualitative descriptions of the classes created and present a conceptual justification f ...
... Similarly, a numerical taxonomy technique can create a classification of entities, and specify a numerical similarity among the entities assembled into the same or different categories, but it cannot alone build qualitative descriptions of the classes created and present a conceptual justification f ...
Nonlinear dimensionality reduction
High-dimensional data, meaning data that requires more than two or three dimensions to represent, can be difficult to interpret. One approach to simplification is to assume that the data of interest lie on an embedded non-linear manifold within the higher-dimensional space. If the manifold is of low enough dimension, the data can be visualised in the low-dimensional space.Below is a summary of some of the important algorithms from the history of manifold learning and nonlinear dimensionality reduction (NLDR). Many of these non-linear dimensionality reduction methods are related to the linear methods listed below. Non-linear methods can be broadly classified into two groups: those that provide a mapping (either from the high-dimensional space to the low-dimensional embedding or vice versa), and those that just give a visualisation. In the context of machine learning, mapping methods may be viewed as a preliminary feature extraction step, after which pattern recognition algorithms are applied. Typically those that just give a visualisation are based on proximity data – that is, distance measurements.