Identifying projected clusters from gene expression profiles

In microarray gene expression data, clusters may hide in subspaces. Traditional clustering algorithms that make use of similarity measurements in the full input space may fail to detect the clusters. In recent years a number of algorithms have been proposed to identify this kind of projected clusters, but many of them rely on some critical parameters whose proper values are hard for users to determine. In this paper a new algorithm that dynamically adjusts its internal thresholds is proposed. It has a low dependency on user parameters while allowing users to input some domain knowledge should they be available. Experimental results show that the algorithm is capable of identifying some interesting projected clusters from real microarray data.published_or_final_versio

On discovery of extremely low-dimensional clusters using semi-supervised projected clustering

Recent studies suggest that projected clusters with extremely low dimensionality exist in many real datasets. A number of projected clustering algorithms have been proposed in the past several years, but few can identify clusters with dimensionality lower than 10% of the total number of dimensions, which are commonly found in some real datasets such as gene expression profiles. In this paper we propose a new algorithm that can accurately identify projected clusters with relevant dimensions as few as 5% of the total number of dimensions. It makes use of a robust objective function that combines object clustering and dimension selection into a single optimization problem. The algorithm can also utilize domain knowledge in the form of labeled objects and labeled dimensions to improve its clustering accuracy. We believe this is the first semi-supervised projected clustering algorithm. Both theoretical analysis and experimental results show that by using a small amount of input knowledge, possibly covering only a portion of the underlying classes, the new algorithm can be further improved to accurately detect clusters with only 1% of the dimensions being relevant. The algorithm is also useful in getting a target set of clusters when there are multiple possible groupings of the objects. © 2005 IEEE.published_or_final_versio

Managing uncertainty of XML schema matching

Despite of advances in machine learning technologies, a schema matching result between two database schemas (e.g., those derived from COMA++) is likely to be imprecise. In particular, numerous instances of "possible mappings" between the schemas may be derived from the matching result. In this paper, we study the problem of managing possible mappings between two heterogeneous XML schemas. We observe that for XML schemas, their possible mappings have a high degree of overlap. We hence propose a novel data structure, called the block tree, to capture the commonalities among possible mappings. The block tree is useful for representing the possible mappings in a compact manner, and can be generated efficiently. Moreover, it supports the evaluation of probabilistic twig query (PTQ), which returns the probability of portions of an XML document that match the query pattern. For users who are interested only in answers with k-highest probabilities, we also propose the top-k PTQ, and present an efficient solution for it. The second challenge we have tackled is to efficiently generate possible mappings for a given schema matching. While this problem can be solved by existing algorithms, we show how to improve the performance of the solution by using a divide-andconquer approach. An extensive evaluation on realistic datasets show that our approaches significantly improve the efficiency of generating, storing, and querying possible mappings. © 2010 IEEE.published_or_final_versionThe IEEE 26th International Conference on Data Engineering (ICDE 2010), Long Beach, CA., 1-6 March 2010. In International Conference on Data Engineering. Proceedings, 2010, p. 297-30

HARP: A practical projected clustering algorithm

In high-dimensional data, clusters can exist in subspaces that hide themselves from traditional clustering methods. A number of algorithms have been proposed to Identify such projected clusters, but most of them rely on some user parameters to guide the clustering process. The clustering accuracy can be seriously degraded If incorrect values are used. Unfortunately, in real situations, it is rarely possible for users to supply the parameter values accurately, which causes practical difficulties in applying these algorithms to real data. In this paper, we analyze the major challenges of projected clustering and suggest why these algorithms need to depend heavily on user parameters. Based on the analysis, we propose a new algorithm that exploits the clustering status to adjust the internal thresholds dynamically without the assistance of user parameters. According to the results of extensive experiments on real and synthetic data, the new method has excellent accuracy and usability. It outperformed the other algorithms even when correct parameter values were artificially supplied to them. The encouraging results suggest that projected clustering can be a practical tool for various kinds of real applications.published_or_final_versio

Acetone in the Atmosphere of Hong Kong, Abundance, Sources and Photochemical Precursors

Intensive field measurements were carried out at a mountain site and an urban site at the foot of the mountain from September to November 2010 in Hong Kong. Acetone was monitored using both canister air samples and 2,4-dinitrophenylhydrazine cartridges. The spatiotemporal patterns of acetone showed no difference between the two sites (p > 0.05), and the mean acetone mixing ratios on O3 episode days were higher than those on non-O3 episode days at both sites (p < 0.05). The source contributions to ambient acetone at both sites were estimated using a receptor model i.e. Positive Matrix Factorization (PMF). The PMF results showed that vehicular emission and secondary formation made the most important contribution to ambient acetone, followed by the solvent use at both sites. However, the contribution of biogenic emission at the mountain site was significantly higher than that at the urban site, whereas biomass burning made more remarkable contribution at the urban site than that at the mountain site. The mechanism of oxidation formation of acetone was investigated using a photochemical box model. The results indicated that i-butene was the main precursor of secondary acetone at the mountain site, while the oxidation of i-butane was the major source of secondary acetone at the urban site.Department of Civil and Environmental Engineerin

Filtering of false positive microRNA candidates by a clustering-based approach

B M C BioinformaticsBackground: MicroRNAs are small non-coding RNA gene products that play diversified roles from species to species. The explosive growth of microRNA researches in recent years proves the importance of microRNAs in the biological system and it is believed that microRNAs have valuable therapeutic potentials in human diseases. Continual efforts are therefore required to locate and verify the unknown microRNAs in various genomes. As many miRNAs are found to be arranged in clusters, meaning that they are in close proximity with their neighboring miRNAs, we are interested in utilizing the concept of microRNA clustering and applying it in microRNA computational prediction. Results: We first validate the microRNA clustering phenomenon in the human, mouse and rat genomes. There are 45.45%, 51.86% and 48.67% of the total miRNAs that are clustered in the three genomes, respectively. We then conduct sequence and secondary structure similarity analyses among clustered miRNAs, non-clustered miRNAs, neighboring sequences of clustered miRNAs and random sequences, and find that clustered miRNAs are structurally more similar to one another, and the RNAdistance score can be used to assess the structural similarity between two sequences. We therefore design a clustering-based approach which utilizes this observation to filter false positives from a list of candidates generated by a selected microRNA prediction program, and successfully raise the positive predictive value by a considerable amount ranging from 15.23% to 23.19% in the human, mouse and rat genomes, while keeping a reasonably high sensitivity. Conclusion: Our clustering-based approach is able to increase the effectiveness of currently available microRNA prediction program by raising the positive predictive value while maintaining a high sensitivity, and hence can serve as a filtering step. We believe that it is worthwhile to carry out further experiments and tests with our approach using data from other genomes and other prediction software tools. Better results may be achieved with fine-tuning of parameters. © 2008 Leung et al; licensee BioMed Central Ltd.published_or_final_versio

Activity-partner recommendation

LNCS v. 9077 entitled: Advances in Knowledge Discovery and Data Mining: 19th Pacific-Asia Conference, PAKDD 2015 ... Proceedings, Part 1In many activities, such as watching movies or having dinner, people prefer to find partners before participation. Therefore, when recommending activity items (e.g., movie tickets) to users, it makes sense to also recommend suitable activity partners. This way, (i) the users save time for finding activity partners, (ii) the effectiveness of the item recommendation is increased (users may prefer activity items more if they can find suitable activity partners), (iii) recommender systems become more interesting and enkindle users' social enthusiasm. In this paper, we identify the usefulness of suggesting activity partners together with items in recommender systems. In addition, we propose and compare several methods for activity-partner recommendation. Our study includes experiments that test the practical value of activity-partner recommendation and evaluate the effectiveness of all suggested methods as well as some alternative strategies.postprin

Clustering uncertain data using voronoi diagrams and R-tree index

We study the problem of clustering uncertain objects whose locations are described by probability density functions (pdfs). We show that the UK-means algorithm, which generalizes the k-means algorithm to handle uncertain objects, is very inefficient. The inefficiency comes from the fact that UK-means computes expected distances (EDs) between objects and cluster representatives. For arbitrary pdfs, expected distances are computed by numerical integrations, which are costly operations. We propose pruning techniques that are based on Voronoi diagrams to reduce the number of expected distance calculations. These techniques are analytically proven to be more effective than the basic bounding-box-based technique previously known in the literature. We then introduce an R-tree index to organize the uncertain objects so as to reduce pruning overheads. We conduct experiments to evaluate the effectiveness of our novel techniques. We show that our techniques are additive and, when used in combination, significantly outperform previously known methods. © 2006 IEEE.published_or_final_versio

Construction of online catalog topologies using decision trees

Organization of a Web site is important to help users get the most out of the site. A good Web site should help visitors find the information they want easily. Visitors typically find information by searching for selected terms of interest or by following links from one Web page to another. The first approach is more useful if the visitor knows exactly what he is seeking, while the second approach is useful when the visitor has less of a preconceived notion about what he wants. The organization of a Web site is especially important in the latter case. Traditionally, Web site organization is done by hand. In this paper, we introduce the problem of automatic Web site construction and propose a solution for solving a major step of the problem based on decision tree algorithms. The solution is found to be useful in automatic construction of product catalogs.published_or_final_versio