Tyne Liang

Fault-tolerant hamiltonian laceability of hypercubes

It is known that every hypercube Qn is a bipartite graph. Assume that n ≥ 2 and F is a subset of edges with |F| ≤ n - 2. We prove that there exists a hamiltonian path in Qn - F between any two vertices of different partite sets. Moreover, there exists a path of length 2n - 2 between any two vertices of the same partite set. Assume that n ≥ 3 and F is a subset of edges with |F| ≤ n - 3. We prove that there exists a hamiltonian path in Qn - {υ} - F between any two vertices in the partite set without υ. Furthermore, all bounds are tight.

An efficient algorithm for mining temporal high utility itemsets from data streams

Utility of an itemset is considered as the value of this itemset, and utility mining aims at identifying the itemsets with high utilities. The temporal high utility itemsets are the itemsets whose support is larger than a pre-specified threshold in current time window of the data stream. Discovery of temporal high utility itemsets is an important process for mining interesting patterns like association rules from data streams. In this paper, we propose a novel method, namely THUI (Temporal High Utility Itemsets)-Mine, for mining temporal high utility itemsets from data streams efficiently and effectively. To the best of our knowledge, this is the first work on mining temporal high utility itemsets from data streams. The novel contribution of THUI-Mine is that it can effectively identify the temporal high utility itemsets by generating fewer candidate itemsets such that the execution time can be reduced substantially in mining all high utility itemsets in data streams. In this way, the process of discovering all temporal high utility itemsets under all time windows of data streams can be achieved effectively with less memory space and execution time. This meets the critical requirements on time and space efficiency for mining data streams. Through experimental evaluation, THUI-Mine is shown to significantly outperform other existing methods like Two-Phase algorithm under various experimental conditions.

Editorial: An integration of WordNet and fuzzy association rule mining for multi-label document clustering

With the rapid growth of text documents, document clustering has become one of the main techniques for organizing large amount of documents into a small number of meaningful clusters. However, there still exist several challenges for document clustering, such as high dimensionality, scalability, accuracy, meaningful cluster labels, overlapping clusters, and extracting semantics from texts. In order to improve the quality of document clustering results, we propose an effective Fuzzy-based Multi-label Document Clustering (FMDC) approach that integrates fuzzy association rule mining with an existing ontology WordNet to alleviate these problems. In our approach, the key terms will be extracted from the document set, and the initial representation of all documents is further enriched by using hypernyms of WordNet in order to exploit the semantic relations between terms. Then, a fuzzy association rule mining algorithm for texts is employed to discover a set of highly-related fuzzy frequent itemsets, which contain key terms to be regarded as the labels of the candidate clusters. Finally, each document is dispatched into more than one target cluster by referring to these candidate clusters, and then the highly similar target clusters are merged. We conducted experiments to evaluate the performance based on Classic, Re0, R8, and WebKB datasets. The experimental results proved that our approach outperforms the influential document clustering methods with higher accuracy. Therefore, our approach not only provides more general and meaningful labels for documents, but also effectively generates overlapping clusters.

Long paths in hypercubes with conditional node-faults

Let F be a set of f ≤ 2 n - 5 faulty nodes in an n-cube Q n such that every node of Q n still has at least two fault-free neighbors. Then we show that Q n - F contains a path of length at least 2 n - 2 f - 1 (respectively, 2 n - 2 f - 2 ) between any two nodes of odd (respectively, even) distance. Since the n-cube is bipartite, the path of length 2 n - 2 f - 1 (or 2 n - 2 f - 2 ) turns out to be the longest if all faulty nodes belong to the same partite set. As a contribution, our study improves upon the previous result presented by J.-S. Fu, Longest fault-free paths in hypercubes with vertex faults, Information Sciences 176 (2006) 759-771] where only n - 2 faulty nodes are considered.

Mining fuzzy frequent itemsets for hierarchical document clustering

As text documents are explosively increasing in the Internet, the process of hierarchical document clustering has been proven to be useful for grouping similar documents for versatile applications. However, most document clustering methods still suffer from challenges in dealing with the problems of high dimensionality, scalability, accuracy, and meaningful cluster labels. In this paper, we will present an effective Fuzzy Frequent Itemset-Based Hierarchical Clustering (F^2IHC) approach, which uses fuzzy association rule mining algorithm to improve the clustering accuracy of Frequent Itemset-Based Hierarchical Clustering (FIHC) method. In our approach, the key terms will be extracted from the document set, and each document is pre-processed into the designated representation for the following mining process. Then, a fuzzy association rule mining algorithm for text is employed to discover a set of highly-related fuzzy frequent itemsets, which contain key terms to be regarded as the labels of the candidate clusters. Finally, these documents will be clustered into a hierarchical cluster tree by referring to these candidate clusters. We have conducted experiments to evaluate the performance based on Classic4, Hitech, Re0, Reuters, and Wap datasets. The experimental results show that our approach not only absolutely retains the merits of FIHC, but also improves the accuracy quality of FIHC.

An efficient algorithm for mining high utility itemsets with negative item values in large databases

Utility itemsets typically consist of items with different values such as utilities, and the aim of utility mining is to identify the itemsets with highest utilities. In the past studies on utility mining, the values of utility itemsets were considered as positive. In some applications, however, an itemset may be associated with negative item values. Hence, discovery of high utility itemsets with negative item values is important for mining interesting patterns like association rules. In this paper, we propose a novel method, namely HUINIV (High Utility Itemsets with Negative Item Values)-Mine, for efficiently and effectively mining high utility itemsets from large databases with consideration of negative item values. To the best of our knowledge, this is the first work that considers the concept of negative item values in utility mining. The novel contribution of HUINIV-Mine is that it can effectively identify high utility itemsets by generating fewer high transaction-weighted utilization itemsets such that the execution time can be reduced substantially in mining the high utility itemsets. In this way, the process of discovering all high utility itemsets with consideration of negative item values can be accomplished effectively with less requirements on memory space and CPU I/O. This meets the critical requirements of temporal and spatial efficiency for mining high utility itemsets with negative item values. Through experimental evaluation, it is shown that HUINIV-Mine outperforms other methods substantially by generating much less candidate itemsets under different experimental conditions.

An integration of fuzzy association rules and WordNet for document clustering

With the rapid growth of text documents, document clustering technique is emerging for efficient document retrieval and better document browsing. Recently, some methods had been proposed to resolve the problems of high dimensionality, scalability, accuracy, and meaningful cluster labels by using frequent itemsets derived from association rule mining for clustering documents. In order to improve the quality of document clustering results, we propose an effective Fuzzy Frequent Itemset-based Document Clustering (F2IDC) approach that combines fuzzy association rule mining with the background knowledge embedded in WordNet. A term hierarchy generated from WordNet is applied to discover generalized frequent itemsets as candidate cluster labels for grouping documents. We have conducted experiments to evaluate our approach on Classic4, Re0, R8, and WebKB datasets. Our experimental results show that our proposed approach indeed provide more accurate clustering results than prior influential clustering methods presented in recent literature.

An Integration of Fuzzy Association Rules and WordNet for Document Clustering

With the rapid growth of text documents, document clustering has become one of the main techniques for organizing large amount of documents into a small number of meaningful clusters. However, there still exist several challenges for document clustering, such as high dimensionality, scalability, accuracy, meaningful cluster labels, and extracting semantics from texts. In order to improve the quality of document clustering results, we propose an effective Fuzzy Frequent Itemset-based Document Clustering (F2IDC) approach that combines fuzzy association rule mining with the background knowledge embedded in WordNet. A term hierarchy generated from WordNet is applied to discovery fuzzy frequent itemsets as candidate cluster labels for grouping documents. We have conducted experiments to evaluate our approach on Reuters-21578 dataset. The experimental result shows that our proposed method outperforms the accuracy quality of FIHC, HFTC, and UPGMA.

Optimal weight assignment for a Chinese signature file

The performance of a character-based Chinese text retrieval scheme (the combined scheme) is investigated. In the scheme both the monogram keys (singleton characters) and bigram keys (consecutive character pairs) are encoded into document signatures such that half of the bits in every signature are set. For disyllabic queries, an analytical expression of the false hit rate that accounts for both random false hits and adjacency false hits is proposed. Then optimal monogram and bigram weight assignments together with the corresponding minimal false hit rate are derived in terms of signature length, storage overhead of the combined scheme, and the occurrence frequency and the association value of a disyllabic query. The theoretical predictions of the optimal weight assignments and the minimal false hit rate are tested and verified in experiments using a real Chinese corpus for disyllabic queries of different association values. Satisfactory agreement between the experimental results and theoretical predictions is found.

Anaphora resolution for biomedical literature by exploiting multiple resources

In this paper, a resolution system is presented to tackle nominal and pronominal anaphora in biomedical literature by using rich set of syntactic and semantic features. Unlike previous researches, the verification of semantic association between anaphors and their antecedents is facilitated by exploiting more outer resources, including UMLS, WordNet, GENIA Corpus 3.02p and PubMed. Moreover, the resolution is implemented with a genetic algorithm on its feature selection. Experimental results on different biomedical corpora showed that such approach could achieve promising results on resolving the two common types of anaphora.