Data clustering techniques

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  • Nowadays, huge amount of multimedia data are being constantly generated in various forms from various places around the world. With ever increasing complexity and variability of multimedia data, traditional rule-based approaches where humans have to discover the domain knowledge and encode it into a set of programming rules are too costly and incompetent for analyzing the contents, and gaining the intelligence of this glut of multimedia data. The challenges in data complexity and variability have led to revolutions in machine learning techniques.

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  • This paper presents an exploratory data analysis in lexical acquisition for adjective classes using clustering techniques. From a theoretical point of view, this approach provides large-scale empirical evidence for a sound classification. From a computational point of view, it helps develop a reliable automatic subclassification method. Results show that the features used in theoretical work can be successfully modelled in terms of shallow cues.

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  • Finding groups of objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups. Understanding Group related documents for browsing, group genes and proteins that have similar functionality, or group stocks with similar price fluctuations Summarization Reduce the size of large data sets

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  • Creates nested clusters Agglomerative clustering algorithms vary in terms of how the proximity of two clusters are computed MIN (single link): susceptible to noise/outliers MAX/GROUP AVERAGE: may not work well with non-globular clusters CURE algorithm tries to handle both problems Often starts with a proximity matrix A type of graph-based algorithm

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  • Statistical machine learning methods are employed to train a Named Entity Recognizer from annotated data. Methods like Maximum Entropy and Conditional Random Fields make use of features for the training purpose. These methods tend to overfit when the available training corpus is limited especially if the number of features is large or the number of values for a feature is large. To overcome this we proposed two techniques for feature reduction based on word clustering and selection.

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  • In statistical language modeling, one technique to reduce the problematic effects of data sparsity is to partition the vocabulary into equivalence classes. In this paper we investigate the effects of applying such a technique to higherorder n-gram models trained on large corpora.

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  • When you have completed this chapter, you will be able to: Organize raw data into frequency distribution; produce a histogram, a frequency polygon, and a cumulative frequency polygon from quantitative data; develop and interpret a stem-and-leaf display; present qualitative data using such graphical techniques such as a clustered bar chart, a stacked bar chart, and a pie chart; detect graphic deceptions and use a graph to present data with clarity, precision, and efficiency.

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  • Cluster Analysis is a technique for classifying data, i.e to divide the given data into a set of classes or clusters.

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  • The need for more rigorous and systematic research in public administration has grown as the complexity of problems in government and nonprofit organizations has increased. This book describes and explains the use of research methods that will strengthen the research efforts of those solving government and nonprofit problems. This book is aimed primarily at those studying research methods in masters and doctoral level courses in curricula that concern the public and nonprofit sector.

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  • Data replication is an increasingly important topic as databases are more and more deployed over clusters of workstations. One of the challenges in database replication is to introduce replication without severely affecting perfor- mance. Because of this difficulty, current database products use lazy replication, which is very efficient but can com- promise consistency. As an alternative, eager replication guarantees consistency but most existing protocols have a prohibitive cost.

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  • Letter-to-phoneme (L2P) conversion is the process of producing a correct phoneme sequence for a word, given its letters. It is often desirable to reduce the quantity of training data — and hence human annotation — that is needed to train an L2P classifier for a new language. In this paper, we confront the challenge of building an accurate L2P classifier with a minimal amount of training data by combining several diverse techniques: context ordering, letter clustering, active learning, and phonetic L2P alignment.

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