19030773
Standardized evaluation method for Web clustering results
Web clustering assists users of a search engine by presenting search results as clusters of related pages. Many clustering algorithms with different characteristics have been developed: but the lack of a standardized Web clustering evaluation method that can evaluate clusterings with different characteristics has prevented effective comparison of algorithms. The paper solves this by introducing a new structure for defining general ideal clusterings and new measurements for evaluating clusterings with different characteristics by comparing them against the general ideal clustering.
39682007
TreeWrapper: Automatic Data Extraction Based on Tree Representation
This paper introduces a new algorithm that learns to extract data from Web pages with relatively regular data structures. Current existing systems require training on either manually labelled pages or at least two similar unlabelled pages, and they often have difficulties on handling Web pages with complex formats such as nested tables or lists. Our previous system AutoWrapper does not need any training and can automatically extract data from any single page. This paper improves AutoWrapper by handling nested structures and finding multiple regular data areas. The main contributions include a tree-based representation for Web pages, an expressive language for representing information extraction patterns, and a learning algorithm that automatically detects regular data areas by finding similar sub-trees.
17022495
Improving Web clustering by cluster selection
Web page clustering is a technology that puts semantically related Web pages into groups and is useful for categorizing, organizing, and refining search results. When clustering using only textual information, suffix tree clustering (STC) outperforms other clustering algorithms by making use of phrases and allowing clusters to overlap. One problem of STC and other similar algorithms is how to select a small set of clusters to display to the user from a very large set of generated clusters. The cluster selection method used in STC is flawed in that it does not handle overlapping clusters appropriately. This paper introduces a new cluster scoring function and a new cluster selection algorithm to overcome the problems with overlapping clusters, which are combined with STC to make a new clustering algorithm ESTC. This paper's experiments show that ESTC significantly outperforms STC and that even with less data ESTC performs similarly to a commercial clustering search engine.
40240913
Combining Contents and Citations for Scientific Document Classification
This paper introduces a classification system that exploits the content information as well as citation structure for scientific paper classification. The system first applies a content-based statistical classification method which is similar to general text classification. We investigate several classification methods including K-nearest neighbours, nearest centroid, naive Bayes and decision trees. Among those methods, the K-nearest neighbours is found to outperform others while the rest perform comparably. Using phrases in addition to words and a good feature selection strategy such as information gain can improve system accuracy and reduce training time in comparison with using words only. To combine citation links for classification, the system proposes an iterative method to update the labellings of classified instances using citation links. Our results show that, combining contents and citations significantly improves the system performance.
1168192
Approximately Repetitive Structure Detection for Wrapper Induction
In recent years, much work has been invested into automatically learning wrappers for information extraction from HTML tables and lists. Our research has focused on a system that can learn a wrapper from a single unlabelled page. An essential step is to locate the tabular data within the page. This is not trivial when the structures of data tuples are similar but not identical. In this paper we describe an algorithm that can automatically detect approximate repetitive structures within one sequence. The algorithm does not rely on any domain knowledge or HTML heuristics and it can be used in detecting repetitive patterns and hence to learn wrappers from a single unlabeled tabular page.
40362881
A Knowledge Learning Approach to Information Extraction from Multiple Text Based Web Sites
This paper describes a learning/adaptive approach to automatically building knowledge bases for information extraction from text based web pages. A frame based representation is introduced to represent domain knowledge as knowledge unit frames. A frame learning algorithm is developed to automatically learn knowledge unit frames from training examples. Some training examples can be obtained by automatically parsing a number of tabular web pages in the same domain, which greatly reduced the amount of time consuming manual work. This approach was investigated on ten web sites of real estate advertisements and car advertisements and nearly all the information was successfully extracted with very few false alarms. These results suggest that both the knowledge unit frame representation and the frame learning algorithm work well, domain specific knowledge bases can be learned from training examples, and the domain specific knowledge base can be used for information extraction from flexible text-based semi-structured Web pages on multiple Web sites. The investigation of the knowledge representation on five other domains suggests that this approach can be easily applied to other domains by simply changing the training examples.
31240676
A Novel Clustering Algorithm Based on Immune Network with Limited Resource
46154955
A Novel Typical-Sample-Weighted Clustering Algorithm for Large Data Sets
In the field of cluster analysis, most of existing algorithms are developed for small data sets, which cannot effectively process the large data sets encountered in data mining. Moreover, most clustering algorithms consider the contribution of each sample for classification uniformly. In fact, different samples should be of different contribution for clustering result. For this purpose, a novel typical-sample-weighted clustering algorithm is proposed for large data sets. By the atom clustering, the new algorithm extracts the typical samples to reduce the data amount. Then the extracted samples are weighted by their corresponding typicality and then clustered by the classical fuzzy c-means (FCM) algorithm. Finally, the Mahalanobis distance is employed to classify each original sample into obtained clusters. It is obvious that the novel algorithm can improve the speed and robustness of the traditional FCM algorithm. The experimental results with various test data sets illustrate the effectiveness of the proposed clustering algorithm.
10271659
Gene2Oligo: oligonucleotide design for in vitro gene synthesis
There is substantial interest in implementing a bioinformatics tool that allows the design of oligonucleotides to support the development of in vitro gene synthesis. Current protocols to make long synthetic DNA molecules rely on the in vitro assembly of a set of short oligonucleotides, either by ligase chain reaction (LCR) or by assembly PCR. Ideally, such oligonucleotides should represent both strands of the final DNA molecule. They should be adjacent on the same strand and overlap the complementary oligonucleotides from the second strand to ensure good hybridization during assembly. This implies that the thermodynamic properties of each oligonucleotide have to be consistent across the set. Furthermore, any given oligonucleotide has to be totally specific to its target to avoid the creation of incorrectly assembled sequences. We have developed Gene2Oligo (http://berry.engin.umich.edu/gene2oligo/), a web-based tool that divides a long input DNA sequence into a set of adjacent oligonucleotides representing both DNA strands. The length of the oligonucleotides is dynamically optimized to ensure both the specificity and the uniform melting temperatures necessary for in vitro gene synthesis. We have successfully designed and used a set of oligonucleotides to synthesize the Saccharomyces cerevisiae cytochrome b5 by using both LCR and assembly PCR.
1210426
De-interlacing Algorithm Based on Motion Objects
A novel de-interlacing algorithm based on motion objects is presented in this paper. In this algorithm, natural motion objects, not contrived blocks, are considered as the processing cells, which are accurately detected by a new scheme, and whose matching objects are quickly searched by the immune clonal selection algorithm. This novel algorithm integrates many other de-interlacing methods, so it is more adaptive to various complex video sequences. Moreover, it can perform the motion compensation for objects with the translation, rotation as well as the scaling transform. The experimental results illustrate that compared with the block matching method with full search, the proposed algorithm greatly improve the efficiency and performance.
206603960
FCM-Based Clustering Algorithm Ensemble for Large Data Sets
In the field of cluster analysis, most of the available algorithms were designed for small data sets, which cannot efficiently deal with large scale data set encountered in data mining. However, some sampling-based clustering algorithms for large scale data set cannot achieve ideal result. For this purpose, a FCM-based clustering ensemble algorithm is proposed. Firstly, it performs the atom clustering algorithm on the large data set. Then, randomly select a sample from each atom as representative to reduce the data amount. And the ensemble learning technique is used to improve the clustering performance. For the complex large data sets, the new algorithm has high classification speed and robustness. The experimental results illustrate the effectiveness of the proposed clustering algorithm.