A multimode network consists of heterogeneous types of actors with various interactions occurring between them. Identifying communities in a multimode network can help understand the structural properties of the network, address the data shortage and unbalanced problems, and assist tasks like targeted marketing and finding influential actors within or between groups. In general, a network […]
Holistic Top-k Simple Shortest Path Join in Graphs
Motivated by the needs such as group relationship analysis, this paper introduces a new operation on graphs, named top-k path join, which discovers the top-k simple shortest paths between two given node sets. Rather than discovering the top-k simple paths between each node pair, this paper proposes a holistic join method which answers the top-k […]
Fuzzy Orders-of-Magnitude-Based Link Analysis for Qualitative Alias Detection
Alias detection has been the significant subject being extensively studied for several domain applications, especially intelligence data analysis. Many preliminary methods rely on text-based measures, which are ineffective with false descriptions of terrorists’ name, date-of-birth, and address. This barrier may be overcome through link information presented in relationships among objects of interests. Several numerical link-based […]
Fast Elastic Peak Detection for Mass Spectrometry
We study a data mining problem concerning the elastic peak detection in 2D liquid chromatography-mass spectrometry (LC-MS) data. These data can be modeled as time series, in which the X-axis represents time points and the Y-axis represents intensity values. A peak occurs in a set of 2D LC-MS data when the sum of the intensity […]
Efficiently Indexing Large Sparse Graphs for Similarity Search
The graph structure is a very important means to model schemaless data with complicated structures, such as protein-protein interaction networks, chemical compounds, knowledge query inferring systems, and road networks. This paper focuses on the index structure for similarity search on a set of large sparse graphs and proposes an efficient indexing mechanism by introducing the […]
Feature Selection Based on Class-Dependent Densities for High-Dimensional Binary Data
Data and knowledge management systems employ feature selection algorithms for removing irrelevant, redundant, and noisy information from the data. There are two well-known approaches to feature selection, feature ranking (FR) and feature subset selection (FSS). In this paper, we propose a new FR algorithm, termed as class-dependent density-based feature elimination (CDFE), for binary data sets. […]
Discover Dependencies from Data—A Review
Functional and inclusion dependency discovery is important to knowledge discovery, database semantics analysis, database design, and data quality assessment. Motivated by the importance of dependency discovery, this paper reviews the methods for functional dependency, conditional functional dependency, approximate functional dependency, and inclusion dependency discovery in relational databases and a method for discovering XML functional dependencies.
Answering General Time-Sensitive Queries
Time is an important dimension of relevance for a large number of searches, such as over blogs and news archives. So far, research on searching over such collections has largely focused on locating topically similar documents for a query. Unfortunately, topic similarity alone is not always sufficient for document ranking. In this paper, we observe […]
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