Supporting decision making in domains in which the observed phenomenon dynamics have to be dealt with, can greatly benefit of retrieval of past cases, provided that proper representation and retrieval techniques are implemented. In particular, when the parameters of interest take the form of time series, dimensionality reduction and flexible retrieval have to be addresses […]
Mining Association Rules between Sets of Items in Large Databases
Frequent itemset mining is a widely exploratory technique that focuses on discovering recurrent correlations among data. The steadfast evolution of markets and business environments prompts the need of data mining algorithms to discover significant correlation changes in order to reactively suit product and service provision to customer needs. Change mining, in the context of frequent itemsets, […]
Query Recommendation Using Query Logs in Search Engines
For a broad-topic and ambiguous query, different users may have different search goals when they submit it to a search engine. The inference and analysis of user search goals can be very useful in improving search engine relevance and user experience. In this paper, we propose a novel approach to infer user search goals by […]
Privacy Preserving Network Publication against Structural Attacks
Privacy is one of the major concerns when publishing or sharing social network data for social science research and business analysis. Recently, researchers have developed privacy models similar to k-anonymity to prevent node re-identification through structure information. However, even when these privacy models are enforced, an attacker may still be able to infer one’s private […]
Mining Distance-Based Outliers from Categorical Data
Outlier detection can usually be considered as a pre-processing step for locating, in a data set, those objects that do not conform to well-defined notions of expected behavior. It is very important in data mining for discovering novel or rare events, anomalies, vicious actions, exceptional phenomena, etc. We are investigating outlier detection for categorical data […]
MIN-MAX: A Counter-Based Algorithm for Regular Expression Matching
We propose an NFA-based algorithm called MIN-MAX to support matching of regular expressions (regexp) composed of Character Classes with Constraint Repetitions (CCR). MIN-MAX is well suited for massive parallel processing architectures, such as FPGAs, yet it is effective on any other computing platform. In MIN-MAX, each active CCR engine (to implement one CCR term) evaluates […]
Tractable Reasoning and Efficient Query Answering in Description Logics
The current trend for building an ontology-based data management system (DMS) is to capitalize on efforts made to design a preexisting well-established DMS (a reference system). The method amounts to extracting from the reference DMS a piece of schema relevant to the new application needs—a module—, possibly personalizing it with extra constraints w.r.t. the application […]
Medical image enhancement using resolution synthesis
We introduce the bilateral filter BF is the same bilateral filter we use in the HSF for improved resolution synthesis For picture blocks, BR(Bayesian reconstruction) simply uses the conventional JPEG decoder to decode the blocks. RS is a classification based image interpolation scheme which achieves optimal interpolation by training from image pairs at low resolution […]
Improved binary partition tree construction for hyperspectral images
The optimal exploitation of the information provided by hyperspectral images requires the development of advanced image-processing tools. Hyperspectral imaging enables the characterization of regions based on their spectral properties which provides a rich amount of information. This paper proposes the construction and the processing of a new region-based hierarchical hyperspectral image representation relying on the […]
Learning Horizontal Connections In A Sparse Coding Model Of Natural Images
Image prior models based on sparse and redundant representations are attracting more and more attention in the field of image restoration. The conventional sparsity-based methods enforce sparsity prior on small image patches independently. Unfortunately, these works neglected the contextual information between sparse representations of neighboring image patches . Sparse coding of image patches extracted from […]