Clustering is the division of data into groups of similar objects. In clustering, some details are disregarded in exchange for data simplification. Clustering can be viewed as a data modeling technique that provides for concise summaries of the data. Clustering is therefore related to many disciplines and plays an important role in a broad range of applications. The applications of clustering usually deal with large datasets and data with many attributes. Exploration of such data is a subject of data mining. This survey concentrates on clustering algorithms from a data mining perspective.

A Survey of Clustering Data Mining Techniques

This paper presents the results of an experimental study of some common document clustering techniques. In particular, we compare the two main approaches to document clustering, agglomerative hierarchical clustering and K-means. (For K-means we used a “standard” K-means algorithm and a variant of K-means, “bisecting” K-means.) Hierarchical clustering is often portrayed as the better quality clustering approach, but is limited because of its quadratic time complexity. In contrast, K-means and its variants have a time complexity which is linear in the number of documents, but are thought to produce inferior clusters. Sometimes K-means and agglomerative hierarchical approaches are combined so as to “get the best of both worlds.” However, our results indicate that the bisecting K-means technique is better than the standard K-means approach and as good or better than the hierarchical approaches that we tested for a variety of cluster evaluation metrics. We propose an explanation for these results that is based on an analysis of the specifics of the clustering algorithms and the nature of document

/pdf/a-comparison-of-document-clustering-techniques-3npklskhy9.pdf

A Comparison of Document Clustering Techniques

Because many animal species are undescribed, and because the identification of known species is often difficult, interim taxonomic nomenclature has often been used in biodiversity analysis. By assigning individuals to presumptive species, called operational taxonomic units (OTUs), these systems speed investigations into the patterning of biodiversity and enable studies that would otherwise be impossible. Although OTUs have conventionally been separated through their morphological divergence, DNA-based delineations are not only feasible, but have important advantages. OTU designation can be automated, data can be readily archived, and results can be easily compared among investigations. This study exploits these attributes to develop a persistent, species-level taxonomic registry for the animal kingdom based on the analysis of patterns of nucleotide variation in the barcode region of the cytochrome c oxidase I (COI) gene. It begins by examining the correspondence between groups of specimens identified to a species through prior taxonomic work and those inferred from the analysis of COI sequence variation using one new (RESL) and four established (ABGD, CROP, GMYC, jMOTU) algorithms. It subsequently describes the implementation, and structural attributes of the Barcode Index Number (BIN) system. Aside from a pragmatic role in biodiversity assessments, BINs will aid revisionary taxonomy by flagging possible cases of synonymy, and by collating geographical information, descriptive metadata, and images for specimens that are likely to belong to the same species, even if it is undescribed. More than 274,000 BIN web pages are now available, creating a biodiversity resource that is positioned for rapid growth.

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A DNA-Based Registry for All Animal Species: The Barcode Index Number (BIN) System

https://www.stat.washington.edu/mmp/Papers/compare-jmva-revised.pdf

Comparing clusterings---an information based distance

Time-series clustering - A decade review

Clustering is a powerful technique for large-scale topic discovery from text. It involves two phases: first, feature extraction maps each document or record to a point in high-dimensional space, then clustering algorithms automatically group the points into a hierarchy of clusters. We describe an unsupervised, near-linear time text clustering system that offers a number of algorithm choices for each phase. We introduce a methodology for measuring the quality of a cluster hierarchy in terms of FMeasure, and present the results of experiments comparing different algorithms. The evaluation considers some feature selection parameters (tfidfand feature vector length) but focuses on the clustering algorithms, namely techniques from Scatter/Gather (buckshot, fractionation, and split/join) and kmeans. Our experiments suggest that continuous center adjustment contributes more to cluster quality than seed selection does. It follows that using a simpler seed selection algorithm gives a better time/quality tradeoff. We describe a refinement to center adjustment, “vector average damping,” that further improves cluster quality. We also compare the near-linear time algorithms to a group average greedy agglomerative clustering algorithm to demonstrate the time/quality tradeoff quantitatively.

/pdf/fast-and-effective-text-mining-using-linear-time-document-3w7tfdcpld.pdf

Fast and effective text mining using linear-time document clustering

We describe a scalable summarization system which takes advantage of robust NLP technology such as corpus-based statlshcal NLP techmques, information extractmn and readily available on-hne resources The system attempts to compensate for the bottlenecks of traditional frequency-based, knowledge-based or discourse-based summanzatlon approaches by uhhzlng features derived by these robust techniques Prehrmnary evaluation results are reported, and the multi-dimensional summary viewer is described 1 I n t r o d u c t i o n Summarization research and system development can be broadly characterized as frequency-based, knowledge-based or discourse-based These categories correspond to a continuum of increasing understanding of a text and increasing complextty in text processing Earliest attempts at summarization (Luhn, 1958, Edmundson, 1969, Rush, Salvador, and Zamora, 1971) essentially rehed on lexlcal and locahonal mformation within the text, 1 e , frequency of words or key terms, their proxnmty, and locatmn within the text More recent adaptations of tlns approach have employed an automated method to combine these types of feature sets through classification techniques (Kupmc, Pedersen, and Chen, 1995) O r have drawn upon tradlhonal information retrieval indexing methods to incorporate knowledge of a text corpus (Brandow, Mltze, and Rau, 1995) To a large extent, these types of shallow approaches are ignorant of dommn knowledge and the text macrostructure They create summaries by extracting sentences from the original document Knowledge-based approaches generally depend on rich domain knowledge sources to interpret the conceptual structure of the text Systems like TOPIC (Relmer and Hahn, 1988), SUSY (Fum, Gmda, and Tasso, 1985) or SCISORS (Ran, Jacobs, and Zermk, 1989) parse domaan specific texts and create conceptual representahons for the generation of text summarms These types of knowledgeba.~d systems apply knowledge of the domain to characterize specific conceptual knowledge of a text Palce (Pvace and Jones, 1993) provides a good example of the role of thts conceptual mformahon and thloff(Rlloff, 1995) gives a method for automahcally identifying relevant concepts lughly correlated with a category of interest Because these systems create a rich conceptual representation, there are multiple ways m whlcha text summary may be created For example, SUMMONS (McKeown and Radev; 1995) generates a text summary from such a template representahon, whle (Maybury, 1995) describes mulhpie methods for selecting events and presenting event summaries Knowledge-based approaches are usually very knowledge-intensive and domvan-specific Discourse-based approaches are grounded m theorms of text cohesion and coherence and vary conmderably m how much they push the lmnts of text understanding and the complemty as well as automahon of that processing Spearheaded by the lack of cohesion and coherence m extracts produced by frequency-based approaches, much of the work typifying discourse-based approaches focuses on lmgmstic processing of the text to identify the best cohesive sentence candidates (Palce, 1990, Johnson et al , 1993) or the best sentence candidates for represent" mg the rhetorical structure of the text (Mnke et al , • 1994) Both approaches revolve parsing the text and analyzing dlscoarse relations to select sentences for extractmn Frequency-based approaches (Brandow, Mltze, and Rau, 1995) may incorporate heurmhcs to handle readabilityrelated issues and knowledge-based approaches • systematically perform discourse processmg m analyzing and condeusmg the text, but m a broad classificatmn schema It is the discoursebased approaches that tend to focus on the text macrostructure and surface clues to that structure At the far end of the continuum lies work by Sparck Jones (Jones, 1993, Jones, 1995) m describing a

Bjornar Larsen

Papers

Fast and effective text mining using linear-time document clustering

A Scalable Summarization System Using Robust NLP