Clustering multi-typed objects in extended star-structured heterogeneous data
Mining richly structured heterogeneous datasets represents a key emerging challenge for data mining. When traditional clustering methods are applied, heterogeneous networks consisting of multiple entities must first be converted to homogeneous networks ...
CD-Tree: A clustering-based dynamic indexing and retrieval approach
In the big data era, the efficient indexing of gradually increasing databases is becoming vitally important for information retrieval. To incrementally adapt to changes of databases, in this paper we propose a novel clustering based dynamic indexing and ...
A social influence based trust model for recommender systems
Trustworthy computing has recently attracted significant interest from researchers in several fields including multi-agent systems, social network analysis, and recommender systems. As an additional dimension of information to past rating history, trust ...
Scalable and practical One-Pass clustering algorithm for recommender system
Recommender systems apply artificial intelligence techniques for filtering unseen information and predict whether a user would like/dislike a given item. K-Means clustering-based recommendation algorithms have been proposed claiming to increase the ...
Collaborative filtering recommendation algorithm based on user fuzzy similarity
In order to reflect the actual case of users' decisions and rating patterns, and solve the sparsity problem of traditional collaborative filtering recommendation algorithms, a trapezoid fuzzy rating model is proposed, which fuzzifies crisp point into ...
A multi-core computing approach for large-scale multi-label classification
Large scale multi-label learning, i.e. the problem of determining the associated set of labels for an instance, is gaining relevance in recent years due to the emergence of several real-world applications. Most notably, the exponential growth of the Social ...
Adaptive algorithms applied to accelerometer biometrics in a data stream context
The use of smartphone devices has increased over the last years, as illustrated by the growth in smartphone sales. These devices are currently used for several services, such as bank account access, social networks and storage of personal information. In ...
Feature bundling in decision tree algorithm
In empirical data modelling, a model of system is built up from a set of cases that the system has observed. Eventually, the performance of the inducted model is dominated by the quality and quantity of observations. Feature transformation methods are widely ...
Community detection in social network by using a multi-objective evolutionary algorithm
Community detection is one of the main challenges in social network analysis. Since the issue of community detection is considered as a NP-hard problem, Evolutionary algorithms have been used as one of the most effective approaches. In this paper, a multi-...
Knowledge discovery of frequent itemsets with low utility for revenue analysis
It is crucial for businesses to identify any revenue bottlenecks generated from itemsets (also called products). For improving revenue, business managers must determine itemsets that should be retained in or eliminated from shops. Therefore, identifying ...
An evidential data fusion method for affective music video retrieval
Affective video retrieval systems seek to retrieve video contents concerning their impact on viewers' emotions. These systems typically apply a multimodal approach that fuses information from different modalities to specify the affect category. The main ...
Incorporating Wikipedia concepts and categories as prior knowledge into topic models
Topic models have been widely applied in discovering topics that underly a collection of documents. Incorporating human knowledge can guide conventional topic models to produce topics which are easily interpreted and semantically coherent. Several ...
