Usage-based PageRank for Web Personalization - delab-auth
... different context, that of web personalization. Web personalization is defined as any action that adapts the information or services provided by a Web site to the needs of a user or a set of users, taking advantage of the knowledge gained from the users’ navigational behavior and individual interest ...
... different context, that of web personalization. Web personalization is defined as any action that adapts the information or services provided by a Web site to the needs of a user or a set of users, taking advantage of the knowledge gained from the users’ navigational behavior and individual interest ...
RSD: Relational subgroup discovery through first
... export a single relation (as a text file) with rows corresponding to individuals and fields containing the truth values of respective features for the given individual. This table is thus a propositionalized representation of the input data and can be used as an input to various attribute-value lear ...
... export a single relation (as a text file) with rows corresponding to individuals and fields containing the truth values of respective features for the given individual. This table is thus a propositionalized representation of the input data and can be used as an input to various attribute-value lear ...
a survey on machine learning techniques for text classification
... learning setting. Naive Bayes is a simple technique for constructing classifiers models that assign class labels to problem instances, represented as vectors of feature values, where the class labels are drawn from some finite set. It is not a single algorithm for training such classifiers, but a fa ...
... learning setting. Naive Bayes is a simple technique for constructing classifiers models that assign class labels to problem instances, represented as vectors of feature values, where the class labels are drawn from some finite set. It is not a single algorithm for training such classifiers, but a fa ...
Educational Data Mining and Learning Analytics: differences
... In the traditional educational model, instructors have the principal role in the learning process. Students are assumed to have basic knowledge and skills, while instructors are expected to share their knowledge and experience. Learning is tested by means of proctored exams and homework. Before the ...
... In the traditional educational model, instructors have the principal role in the learning process. Students are assumed to have basic knowledge and skills, while instructors are expected to share their knowledge and experience. Learning is tested by means of proctored exams and homework. Before the ...
Data warehousing and data mining
... Presentation: decision-tree, classification rule, neural network ...
... Presentation: decision-tree, classification rule, neural network ...
Shiniphy - Visual Data Mining of movie recommendations
... we expected, like in the number of ratings per movie and the number of ratings per user. This data set is interesting because there were also some outliers in it, like 2 users who rated over 17,000 movies each. We could have removed such extreme cases and pruned the dataset to achieve incrementally ...
... we expected, like in the number of ratings per movie and the number of ratings per user. This data set is interesting because there were also some outliers in it, like 2 users who rated over 17,000 movies each. We could have removed such extreme cases and pruned the dataset to achieve incrementally ...
Towards Big Business Process Mining
... any organization in any field. The power of these techniques comes from the fact that they are based on factbased data [1]. Most of the time, it is very difficult to know all the facts of a given situation. One of the most important reasons for this is the inability of current technology to host hug ...
... any organization in any field. The power of these techniques comes from the fact that they are based on factbased data [1]. Most of the time, it is very difficult to know all the facts of a given situation. One of the most important reasons for this is the inability of current technology to host hug ...
A survey on mining multiple data sources
... and ‘arbiter’ models in a bottom-up tree manner. The focus of metalearning is to combine the predictions of the learned models from the partitioned data subsets in a parallel and distributed environment. Kargupta and his colleagues20,21 considered a collective framework to address data analysis for ...
... and ‘arbiter’ models in a bottom-up tree manner. The focus of metalearning is to combine the predictions of the learned models from the partitioned data subsets in a parallel and distributed environment. Kargupta and his colleagues20,21 considered a collective framework to address data analysis for ...
Association and Sequence Mining in Web Usage
... [1]. For sessions’ identification in the first case was considered that a user can not be stationed on a page more than 30 minutes. This value is used in several previous studies, as can be seen in the work [2]. The current study intends to add an improvement ...
... [1]. For sessions’ identification in the first case was considered that a user can not be stationed on a page more than 30 minutes. This value is used in several previous studies, as can be seen in the work [2]. The current study intends to add an improvement ...
Data Mining – Preprocessing Why Data
... Given N data vectors from k-dimensions, find c <= k orthogonal vectors that can be best used to represent data ...
... Given N data vectors from k-dimensions, find c <= k orthogonal vectors that can be best used to represent data ...
Using Data Mining to Predict Possible Future Depression Cases
... Data Mining is a multidisciplinary field that is based on various fields including database management systems, artificial intelligence, machine learning, neural networks, statistics, pattern recognition, knowledge-based systems, knowledge acquisition, information retrieval, high-performance computi ...
... Data Mining is a multidisciplinary field that is based on various fields including database management systems, artificial intelligence, machine learning, neural networks, statistics, pattern recognition, knowledge-based systems, knowledge acquisition, information retrieval, high-performance computi ...
COMP3420: dvanced Databases and Data Mining
... statisticians and machine learning researchers Scalability: classifying data sets with millions of examples and hundreds of attributes with reasonable speed Why decision tree induction in data mining? Relatively fast learning speed (compared to other classification methods) Convertible to si ...
... statisticians and machine learning researchers Scalability: classifying data sets with millions of examples and hundreds of attributes with reasonable speed Why decision tree induction in data mining? Relatively fast learning speed (compared to other classification methods) Convertible to si ...
From Data Mining to Knowledge Discovery in Databases
... Data Mining and KDD Historically, the notion of finding useful patterns in data has been given a variety of names, including data mining, knowledge extraction, information discovery, information harvesting, data archaeology, and data pattern processing. The term data mining has mostly been used by s ...
... Data Mining and KDD Historically, the notion of finding useful patterns in data has been given a variety of names, including data mining, knowledge extraction, information discovery, information harvesting, data archaeology, and data pattern processing. The term data mining has mostly been used by s ...
Predicting breast cancer survivability
... Objective: The prediction of breast cancer survivability has been a challenging research problem for many researchers. Since the early dates of the related research, much advancement has been recorded in several related fields. For instance, thanks to innovative biomedical technologies, better expla ...
... Objective: The prediction of breast cancer survivability has been a challenging research problem for many researchers. Since the early dates of the related research, much advancement has been recorded in several related fields. For instance, thanks to innovative biomedical technologies, better expla ...
Real Time Data Mining-based Intrusion Detection
... methods, making them unusable in real environments. Also, these systems tend to be inefficient (i.e., computationally expensive) during both training and evaluation. This prevents them from being able to process audit data and detect intrusions in real time. Finally, these systems require large amou ...
... methods, making them unusable in real environments. Also, these systems tend to be inefficient (i.e., computationally expensive) during both training and evaluation. This prevents them from being able to process audit data and detect intrusions in real time. Finally, these systems require large amou ...
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.