6926758
Semantic Correlation Network Based Text Clustering
Text documents have sparse data spaces, and nearest neighbors may belong to different classes when using current existing proximity measures to describe the correlation of documents. In this paper, we propose an asymmetric similarity measure to strengthen the discriminative feature of document objects. We construct a semantic correlation network by asymmetric similarity between documents and conjecture the power law feature of the connections distributions. Hub points which exist in semantic correlation network are classified by an agglomerative hierarchical clustering approach named SCN. Both objects similarity and neighbors similarity are considered in the definition of hub points proximity. Finally, we assign the rest text objects to their nearest hub points. The experimental evaluation on textual data sets demonstrates the validity and efficiency of SCN. The comparison with other clustering algorithms shows the superiority of our approach.
17783880
TCUAP: A Novel Approach of Text Clustering Using Asymmetric Proximity
Text documents have sparse data spaces and current existing methods of text clustering use symmetry proximity to measure the correlation of documents. In this paper, we propose a novel approach to strengthen the discriminative feature of document objects, which uses asymmetric proximity for text clustering. We present a measure of asymmetric proximity between documents and between clusters. TCUAP is an agglomerative hierarchical clustering algorithm and carries on the clustering analysis by strong components of sparse matrix. The experimental evaluation on textual data sets demonstrates the validity and efficiency of our approach. The result shows that the measure of asymmetric proximity possesses higher accuracy than that of symmetry proximity.
2193712
Similarity Joins of Text with Incomplete Information Formats
Similarity join over text is important in text retrieval and query. Due to the incomplete formats of information representation, such as abbreviation and short word, similarity joins should address an asymmetric feature that these incomplete formats may contain only partial information of their original representation. Current approaches, including cosine similarity with q-grams, can hardly deal with the asymmetric feature of similarity between words and their incomplete formats. In order to find this type of incomplete format information with asymmetric features, we develop a new similarity join algorithm, namely IJoin. A novel matching scheme is proposed to identify the overlap between two entities with incomplete formats. Other than q-grams, we reconnect the sequence of words in a string to reserve the abbreviated information. Based on the asymmetric features of similar entities with incomplete formats, we adopt a new similarity function. Furthermore, an efficient algorithm is implemented by using the join operation in SQL, which reduces pairs of tuples in similarity comparison. The experimental evaluation demonstrates the effectiveness and the efficiency of our approach.
16137657
A Parallel Wavefront Algorithm for Efficient Biological Sequence Comparison
In this paper we present a parallel wavefront algorithm for computing an alignment between two strings A and C, with |A| = m and |C| = n. On a distributed memory parallel computer of p processors each with O((m + n)/p) memory, the proposed algorithm requires O(p) communication rounds and O(mn/p) local computing time. The novelty of this algorithm is based on a compromise between the workload of each processor and the number of communication rounds required, expressed by a parameter called α. The proposed algorithm is expressed in terms of this parameter that can be tuned to obtain the best overall parallel time in a given implementation. We show very promising experimental results obtained on a 64-node Beowulf machine. A characteristic of the wavefront communication requirement is that each processor communicates with few other processors. This makes it very suitable as a potential application for grid computing.
6031717
A parallel approximation hitting set algorithm for gene expression analysis
With the recent DNA-microarray technology, it is possible to measure the expression levels of thousands of genes simultaneously in the same experiment. A genetic network is a model that describes how the expression level of each gene is affected by the expression levels of other genes in the network. Given the results of an experiment with n genes and m measures over time (m << n), we consider the problem of finding a subset of genes (k genes, where k < < n) that explain the expression level of a given target gene under study. We consider the coarse-grained multicomputer (CGM) model, with p processors. In this paper we first present a sequential approximation algorithm of O(m/sup 4/n) time and O(m/sup 2/n) space. The main result is a new parallel approximation algorithm that determines the k genes in O(m/sup 4/n/p) local computing time plus O(k) communication rounds, and with space requirement of O(m/sup 2/n/p). The p factor in the parallel time and space complexities indicates a good parallelization. We also show preliminary promising experimental results on a Beowulf machine. To our knowledge there are no CGM algorithms for the problem considered in this paper.
3501095
A BSP/CGM algorithm for the all-substrings longest common subsequence problem
Given two strings X and Y of lengths m and n, respectively, the all-substrings longest common subsequence (ALCS) problem obtains the lengths of the subsequences common to X and any substring of Y. The sequential algorithm takes O(mn) time and O(n) space. We present a parallel algorithm for ALCS on a coarse-grained multicomputer (BSP/CGM) model with p < /spl radic/m processors that takes O(mn/p) time and O(n/spl radic/m) space per processor, with O(log p) communication rounds. The proposed parallel algorithm also solves the well-known LCS problem. To our knowledge this is the best BSP/CGM algorithm for the ALCS problem in the literature.
15186481
Revisiting cycle shrinking
Several loop transformation techniques have been designed to extract parallelism from nested loop structures. We first review two important approaches, known as Generalized Cycle Shrinking presented by Shang, O'Keefe and Fortes and the Index Shift Method introduced by Liu, Ho and Sheu. The main result of the paper is a new methodology that permits to combine cycle shrinking techniques with the index shift method. We present a new optimization method that produces the best scheduling vector, and we show that we can outperform previous results by an arbitrary speedup factor.