Clustering files of chemical structures using the Szekely-Rizzo generalization of Ward's method

Ward's method is extensively used for clustering chemical structures represented by 2D fingerprints. This paper compares Ward clusterings of 14 datasets (containing between 278 and 4332 molecules) with those obtained using the Szekely–Rizzo clustering method, a generalization of Ward's method. The clusters resulting from these two methods were evaluated by the extent to which the various classifications were able to group active molecules together, using a novel criterion of clustering effectiveness. Analysis of a total of 1400 classifications (Ward and Székely–Rizzo clustering methods, 14 different datasets, 5 different fingerprints and 10 different distance coefficients) demonstrated the general superiority of the Székely–Rizzo method. The distance coefficient first described by Soergel performed extremely well in these experiments, and this was also the case when it was used in simulated virtual screening experiments

The Effectiveness of Query-Based Hierarchic Clustering of Documents for Information Retrieval

Hierarchic document clustering has been applied to Information Retrieval (IR) for over three decades. Its introduction to IR was based on the grounds of its potential to improve the effectiveness of IR systems. Central to the issue of improved effectiveness is the Cluster Hypothesis. The hypothesis states that relevant documents tend to be highly similar to each other, and therefore tend to appear in the same clusters. However, previous research has been inconclusive as to whether document clustering does bring improvements. The main motivation for this work has been to investigate methods for the improvement of the effectiveness of document clustering, by challenging some assumptions that implicitly characterise its application. Such assumptions relate to the static manner in which document clustering is typically performed, and include the static application of document clustering prior to querying, and the static calculation of interdocument associations. The type of clustering that is investigated in this thesis is query-based, that is, it incorporates information from the query into the process of generating clusters of documents. Two approaches for incorporating query information into the clustering process are examined: clustering documents which are returned from an IR system in response to a user query (post-retrieval clustering), and clustering documents by using query-sensitive similarity measures. For the first approach, post-retrieval clustering, an analytical investigation into a number of issues that relate to its retrieval effectiveness is presented in this thesis. This is in contrast to most of the research which has employed post-retrieval clustering in the past, where it is mainly viewed as a convenient and efficient means of presenting documents to users. In this thesis, post-retrieval clustering is employed based on its potential to introduce effectiveness improvements compared both to static clustering and best-match IR systems. The motivation for the second approach, the use of query-sensitive measures, stems from the role of interdocument similarities for the validity of the cluster hypothesis. In this thesis, an axiomatic view of the hypothesis is proposed, by suggesting that documents relevant to the same query (co-relevant documents) display an inherent similarity to each other which is dictated by the query itself. Because of this inherent similarity, the cluster hypothesis should be valid for any document collection. Past research has attributed failure to validate the hypothesis for a document collection to characteristics of the collection. Contrary to this, the view proposed in this thesis suggests that failure of a document set to adhere to the hypothesis is attributed to the assumptions made about interdocument similarity. This thesis argues that the query determines the context and the purpose for which the similarity between documents is judged, and it should therefore be incorporated in the similarity calculations. By taking the query into account when calculating interdocument similarities, co-relevant documents can be "forced" to be more similar to each other. This view challenges the typically static nature of interdocument relationships in IR. Specific formulas for the calculation of query-sensitive similarity are proposed in this thesis. Four hierarchic clustering methods and six document collections are used in the experiments. Three main issues are investigated: the effectiveness of hierarchic post-retrieval clustering which uses static similarity measures, the effectiveness of query-sensitive measures at increasing the similarity of pairs of co-relevant documents, and the effectiveness of hierarchic clustering which uses query-sensitive similarity measures. The results demonstrate the effectiveness improvements that are introduced by the use of both approaches of query-based clustering, compared both to the effectiveness of static clustering and to the effectiveness of best-match IR systems. Query-sensitive similarity measures, in particular, introduce significant improvements over the use of static similarity measures for document clustering, and they also significantly improve the structure of the document space in terms of the similarity of pairs of co-relevant documents. The results provide evidence for the effectiveness of hierarchic query-based clustering of documents, and also challenge findings of previous research which had dismissed the potential of hierarchic document clustering as an effective method for information retrieval

Clustering Based Classification and Analysis of Data

This paper presents Clustering Based Document classification and analysis of data. The proposed Clustering Based classification and analysis of data approach is based on Unsupervised and Supervised Document Classification. In this paper Unsupervised Document and Supervised Document Classification are used. In this approach Document collection, Text Preprocessing, Feature Selection, Indexing, Clustering Process and Results Analysis steps are used. Twenty News group data sets [20] are used in the Experiments. For experimental results analysis evaluated using the Analytical SAS 9.0 Software is used. The Experimental Results show the proposed approach out performs

Clustering in massive data sets

We review the time and storage costs of search and clustering algorithms. We exemplify these, based on case-studies in astronomy, information retrieval, visual user interfaces, chemical databases, and other areas. Theoretical results developed as far back as the 1960s still very often remain topical. More recent work is also covered in this article. This includes a solution for the statistical question of how many clusters there are in a dataset. We also look at one line of inquiry in the use of clustering for human-computer user interfaces. Finally, the visualization of data leads to the consideration of data arrays as images, and we speculate on future results to be expected here

Analyzing The Strength Between Mission And Vision Statements And Industry Via Machine Learning

Mission and vision statements are critical to a company’s success both from a company’s long-term goals and appearance to potential customers. We analyze a collection of 772 mission and vision statements from companies via natural language processing. This data is hand annotated into 15 industry types. We show the distinctiveness and connectiveness of each industry via text processing and machine learning techniques. The extracted features of each industry are a telling and guiding indicator of what that industry embraces. We show high predictive power via machine learning to determine an industry by looking only at the mission and vision statement

Clustering for 2D chemical structures

The clustering of chemical structures is important and widely used in several areas of chemoinformatics. A little-discussed aspect of clustering is standardization, it ensures all descriptors in a chemical representation make a comparable contribution to the measurement of similarity. The initial study compares the effectiveness of seven different standardization procedures that have been suggested previously, the results were also compared with unstandardized datasets. It was found that no one standardization method offered consistently the best performance. Comparative studies of clustering effectiveness are helpful in providing suitability and guidelines of different methods. In order to examine the suitability of different clustering methods for the application in chemoinformatics, especially those had not previously been applied to chemoinformatics, the second piece of study carries out an effectiveness comparison of nine clustering methods. However, the result revealed that it is unlikely that a single clustering method can provide consistently the best partition under all circumstances. Consensus clustering is a technique to combine multiple input partitions of the same set of objects to achieve a single clustering that is expected to provide a more robust and more generally effective representation of the partitions that are submitted. The third piece of study reports the use of seven different consensus clustering methods which had not previously been used on sets of chemical compounds represented by 2D fingerprints. Their effectiveness was compared with some traditional clustering methods discussed in the second study. It was observed that no consistently best consensus clustering method was found

Some problems in the theory and application of the methods of numerical taxonomy

Several of the methods of numerical taxonomy are compared and shown to be variants of a tripartite grouping procedure associated with a generalised intercluster similarity function involving ten computational parameters. Clustering by the techniques of hierarchic fusion, monothetic division and iterative relocation is obtained using different arithmetic combinations of the function parameters to both compute similarities and effect changes in cluster membership. The combinatorial solution for Ward's method is found, and the centroid sorting combinatorial solution is extended for size difference, shape difference, dispersion and dot product coefficients. It is suggested that clusters are characterised more by the choice of similarity criterion than by the choice of method, and it is demonstrated that some common criteria such as distance and the error sum of squares are inclined to force spherical 'minimum-variance' classes. These are contrasted by 'natural' classes, which correspond to closed density surfaces defined for a multi-variate sample space by the underlying probability density function. A method for mode-seeking is developed from this probabilistic model through various theoretical and experimental phases, and it is shown to perform slightly better than iterative relocation with the minimum-variance criteria using several Gaussian test populations. A fast algorithm is proposed for the solution of the Jardine-Sibson method for generating overlapping classes, and it is observed that this technique finds natural classes and is closely related to the probabilistic model. Some aspects of computational procedures are discussed, and in particular, it is proposed that a generalised system involving a statistical language, conversational mode package and program suite could be developed from a basic subroutine system. Paging and simulation techniques for the organisation of direct-access data files are suggested, and a comprehensive package of computer programs for cluster analysis is described

Clustering Information Retrieval Search Outputs

Users are known to have difficulties in dealing with information retrieval search outputs especially if the outputs are above a certain size. It has been argued by several researchers that search output clustering can help users in their interaction with IR systems. Clustering may provide users an overview of the output by exploiting the topicality information that resides in the output but has not been used in the retrieval stage. It can enable them to find the relevant documents more easily and also help them to form an understanding of the different facets of the query that have been provided for their Inspection. This project aimed to investigate the viability of using clustering as a way of mediating users’ interaction with search outputs and attempted to identify its possible benefits. Can&Ozkarahan’s(90) C3M algorithm was used to test the effectiveness of clustering as a way of search output presentation. C3M is a relatively simple, non-hierarchical method that has been shown to give compatible or superior results to best-known hierarchical methods. The method was implemented in TCL and linked to the department’s experimental IR system Okapi. Implementation included a procedure of term selection for document representation which preceded the clustering process and a procedure involving cluster representation for users’ viewing following the clustering process. After some tuning of the implementation parameters for the databases used, several experiments were designed and conducted to assess whether clusters could group documents in useful ways. One group of experiments aimed to assess the ability of the implementation to bring together topically related documents. It was quite difficult to gather data for such an assessment, but the existence of a set of data generated for TREC Interactive track(1996) enabled us to design experiments that at least approximately satisfied our objective. TREC provided a set of queries, and groups of relevant documents with facet assignments made by expert users. It was thus possible to make an Inference by measuring the correlation between the clusters relevant documents were assigned to and the facet assignments made for the documents by TREC experts. The utility of this data set was limited for various reasons discussed in the related chapters, however, it can be concluded that clusters cannot be relied on to bring together relevant documents assigned to a certain facet. While there was some correlation between the cluster and facet assignments of the documents when the clustering was done only on relevant documents, no correlation could be found when the clustering was based on results of queries defined by City participants to the Interactive track. Another group of experiments was conducted to compare output clustering with relevance ranking as a search output representation method. This comparison was necessary as an immediate consequence of clustering search output would be the loss of relevance ranking. It had to be assessed whether clustering could help users to find the relevant documents more easily than by relevance ranking, before any clustering solution could be proposed as an alternative to relevance ranked output. For this purpose, two sets of user experiments(n=20 and n=57) were conducted based on the users’ own information needs. While changes have been made to the implementation between the first and the second set of experiments, the experimental design was almost the same in both runs. Users were first asked to rank clusters formed from the search output(top 50 documents) and then make relevance judgements for the individual documents for the same output. The precision of cluster(s) marked best by the users were then compared to precision values that would be attained by relevance ranking at comparable thresholds. The results from the 1st group of user experiments were not conclusive(in some part due to the smallness of the data set), but they drew our attention to the importance of representation of clusters and documents for users’ viewing. After some changes to the implementation, mainly related to representation issues, and an intermediate set of 10 experiments to assess two new representation formats, a set of 57 user experiments were conducted to measure and compare precision values attainable by clustering versus relevance ranking. These experiments revealed no significant precision difference between clustered outputs and ranked lists. The number of cases where one method achieved better than the other was slightly higher for the ranked lists at the top cluster level and slightly higher for the clustered representation at the top two clusters level. However the overall average precision values were higher for the ranked list at both levels. As such, clustering did not appear to be preferable to ranked lists especially as It also represented overheads in both computing time and resources involved in creation of the clusters, and the time and effort taken by the users to inspect them. An interesting outcome of the user experiments was the ability of the users to identify clusters that do not include relevant information. There were less relevant documents among the clusters marked last by the users as compared to the documents ranked last at similar threshold levels. This brought out the possibility of using clusters as an exclusion tool to improve the precision of ranked lists. After exclusion of documents from the last cluster, ranked lists performed significantly better than the clusters at the top cluster level. There was also some evidence (consisting of observation of users during the experiments and a few user comments) that clusters could be used to provide the users with a glimpse of the search results, in order to decide whether to inspect the search results or initiate a new query straight away. In summary, cumulative experiment results imply that clustering cannot outperform relevance ranking, and seems to deserve only a secondary role in users’ interaction with IR systems. However, it should also be noted that the experiment results are not representative of the whole set of possible user types and search situations and it may be possible to Identify search situations where clustering can be more beneficial than relevance ranking