Feature selection is widely used in preparing high-dimensional data for effective data mining. Getting fast popularity in the social media dataset presents new challenges for feature selection. Web search media data consists of... more
Feature selection is widely used in preparing high-dimensional data for effective data mining. Getting fast popularity in the social media dataset presents new challenges for feature selection. Web search media data consists of traditional high- dimensional, attribute-value data such as posts, tweets, comments, and images and linked data. The FAST algorithm applies to on data set and produces smaller subsets of features and also improves the accuracy. The FAST not only produces smaller subsets of features but also improves the performances of the process. Experimental results show that our FAST algorithm implementation can run faster and obtain better-extracted features than other methods.
One of the fundamental problems in graph databases is similarity search for graphs of interest. Existing approaches dealing with this problem rely on a single similarity measure between graph structures. In this paper, we suggest an... more
One of the fundamental problems in graph databases is similarity search for graphs of interest. Existing approaches dealing with this problem rely on a single similarity measure between graph structures. In this paper, we suggest an alternative approach allowing for searching similar graphs to a graph query where similarity between graphs is rather modeled by a vector of scalars than a unique scalar. To this end, we introduce the notion of similarity skyline of a graph query defined by the subset of graphs of the target database that are the most similar to the query in a Pareto sense. The idea is to achieve a d-dimensional comparison between graphs in terms of d local distance (or similarity) measures and to retrieve those graphs that are maximally similar in the sense of the Pareto dominance relation. A diversity-based method for refining the retrieval result is proposed as well.
A user can formulate database queries and updates graphically, by manipulating schema diagrams. The authors based the graphical data manipulation interface on the entity-relationship (ER) model because of its widespread use and increasing... more
A user can formulate database queries and updates graphically, by manipulating schema diagrams. The authors based the graphical data manipulation interface on the entity-relationship (ER) model because of its widespread use and increasing popularity. They use an extended ER model incorporating various forms of generalization and specialization, including subset, union and partition relationships. They call their model the extended conceptual entity-relationship or ECER model. A comparison with other graphical entity-relationship interfaces is included.>
One of the fundamental problems in graph databases is similarity search for graphs of interest. Existing approaches dealing with this problem rely on a single similarity measure between graph structures. In this paper, we suggest an... more
One of the fundamental problems in graph databases is similarity search for graphs of interest. Existing approaches dealing with this problem rely on a single similarity measure between graph structures. In this paper, we suggest an alternative approach allowing for searching similar graphs to a graph query where similarity between graphs is rather modeled by a vector of scalars than a unique scalar. To this end, we introduce the notion of similarity skyline of a graph query defined by the subset of graphs of the target database that are the most similar to the query in a Pareto sense. The idea is to achieve a d-dimensional comparison between graphs in terms of d local distance (or similarity) measures and to retrieve those graphs that are maximally similar in the sense of the Pareto dominance relation. A diversity-based method for refining the retrieval result is proposed as well.
