IEEE Transactions on Knowledge and Data Engineering

Because learning an optimal Bayesian network classifier is an NP-hard problem, learning-improved naive Bayes has attracted much attention from researchers. In this paper, we summarize the existing improved algorithms and propose a novel Bayes model: hidden naive Bayes (HNB). In HNB, a hidden parent is created for each attribute which combines the influences from all other attributes. We experiment... View full abstract»

Data intensive peer-to-peer (P2P) networks are finding increasing number of applications. Data mining in such P2P environments is a natural extension. However, common monolithic data mining architectures do not fit well in such environments since they typically require centralizing the distributed data which is usually not practical in a large P2P network. Distributed data mining algorithms that a... View full abstract»

One of the simplest ways to query a database is through a form where a user can fill in relevant information and obtain desired results by submitting the form. Designing good forms is a nontrivial manual task, and the designer needs a sound understanding of both the data organization and the querying needs. Furthermore, form design usually has conflicting goals: each form should be simple and easy... View full abstract»

In this paper, we propose a distributed method to control the view divergence of data freshness for clients in replicated database systems whose facilitating or administrative roles are equal. Our method provides data with statistically defined freshness to clients when updates are initially accepted by any of the replicas, and then, asynchronously propagated among the replicas that are connected ... View full abstract»

Frequent item set mining is one of the most challenging issues for descriptive data mining. In general, its resulting set tends to produce a large number of frequent item sets. To represent them in a more compact notation, closed or maximal frequent item sets are often used but finding such item sets over online transactional data streams is not easy due to the requirements of a data stream. For t... View full abstract»

In this paper, we first study an important but unsolved dilemma in the literature of subspace clustering, which is referred to as ldquoinformation overlapping-data coveragerdquo challenge. Current solutions of subspace clustering usually invoke a grid-based apriori-like procedure to identify dense regions and construct subspace clusters afterward. Due to the nature of monotonicity property in apri... View full abstract»

Similarity search usually encounters a serious problem in the high-dimensional space, known as the "curse of dimensionality". In order to speed up the retrieval efficiency, most previous approaches reduce the dimensionality of the entire data set to a fixed lower value before building indexes (referred to as global dimensionality reduction (GDR)). More recent works focus on locally reducing the di... View full abstract»

Granular computing and knowledge reduction are two basic issues in knowledge representation and data mining. Granular structure of concept lattices with application in knowledge reduction in formal concept analysis is examined in this paper. Information granules and their properties in a formal context are first discussed. Concepts of a granular consistent set and a granular reduct in the formal c... View full abstract»

The problem of feature selection is a difficult combinatorial task in machine learning and of high practical relevance. In this paper, we consider feature selection method for multimodally distributed data, and present a large margin feature weighting method for k-nearest neighbor (kNN) classifiers. The method learns the feature weighting factors by minimizing a cost function, which aims at separa... View full abstract»

Modern processors have multiple pipelined functional units and can issue more than one instruction per clock cycle. This places a burden on the compiler to schedule the instructions to take maximum advantage of the underlying hardware. Superblocks - a straight-line sequence of code with a single entry point and multiple possible exit points - are a commonly used scheduling region within compilers.... View full abstract»