Understanding Search Engines

Berry, Michael W.; Browne, Murray

doi:10.1137/1.9780898718164

Cited by 110 publications

(51 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The divisive clustering in contrast is a top-down strategy; it starts with a single cluster containing all data points and recursively splits that cluster into appropriate subclusters [57,58]. CURE (Clustering Using Representatives) [59,60] and SVD (Singular Value Decomposition) [61] are typical research.…”

Section: Clustering Clustering Algorithmsmentioning

confidence: 99%

Data Mining for the Internet of Things: Literature Review and Challenges

Chen

Deng

Wan

et al. 2015

International Journal of Distributed Sensor Networks

405

215

View full text Add to dashboard Cite

The massive data generated by the Internet of Things (IoT) are considered of high business value, and data mining algorithms can be applied to IoT to extract hidden information from data. In this paper, we give a systematic way to review data mining in knowledge view, technique view, and application view, including classification, clustering, association analysis, time series analysis and outlier analysis. And the latest application cases are also surveyed. As more and more devices connected to IoT, large volume of data should be analyzed, the latest algorithms should be modified to apply to big data. We reviewed these algorithms and discussed challenges and open research issues. At last a suggested big data mining system is proposed.

show abstract

Section: Clustering Clustering Algorithmsmentioning

confidence: 99%

Data Mining for the Internet of Things: Literature Review and Challenges

Chen

Deng

Wan

et al. 2015

International Journal of Distributed Sensor Networks

405

215

View full text Add to dashboard Cite

show abstract

“…There are more sophisticated schemes such as those given in [1,2] that may lead to better results, and in general, as is done in [2], the procedure is to define the inputs from A to…”

Section: Matrix Term Documentmentioning

confidence: 99%

“…Local and global weights are applied to increase or decrease the importance of terms within or between documents. In [1], they explain the weight scheme for terms that are recommended to use depending on the characteristics of the document collection. In the language of the vector model, the columns and rows of A are called document vectors and term vectors, respectively.…”

Section: Matrix Term Documentmentioning

confidence: 99%

“…This factorization exists for any matrix A, and numerical linear algebra texts commonly include it in their content [7,8]. Methods to calculate the SVD of dense and sparse matrices are well documented [1,6,7].…”

Section: Singular Value Decompositionmentioning

confidence: 99%

“…The first three indicate the local weight, the global weight and the use of standardization in the matrix term document, respectively, and the last corresponds to the query matrix and refers to the local weight of the terms. In this way, the nomenclature fex.l, for example, means that in the matrix term document, the frequency (f) for the local weight and an entropy value (e) (see [1]) for the global weight of the terms were used; besides, the columns of the matrix document term were not normalized (x) and a logarithmic value (l) was used for the local weight of the query terms. In this case, different LSI models are tested and the best one is determined from the MAP for k = 100.…”

Section: Case 1: Identification Of the Lsi Modelmentioning

confidence: 99%

See 2 more Smart Citations

Cross - Language Information Retrieval Using Two Methods: LSI via SDD and LSI via SVD

Lopez¹,

Blanquicett²,

Medina³

2018

Multilingualism and Bilingualism

View full text Add to dashboard Cite

This chapter presents a method for the recovery of bilingual information based on semidiscrete matrix decomposition (SDD); that is, the problem of retrieving information in two languages, Spanish and English, is studied when the queries are made only in Spanish. In it, four case studies that exhibit the performance of the use of the latent semantic index (LSI) via SDD method for cross-language information retrieval (CLIR) are displayed. Concurrently, these results are compared with those obtained by applying LSI via singular value decomposition (SVD). All experiments were performed from a bilingual database, built from the gospels of the Bible, which combines documents in Spanish and English. For this, a fusion strategy was used that increases the size of the database by 10%. It was found that in terms of errors, the methods are comparable, since equal results were obtained in 58.3% of the queries made. In addition, the methods presented a success rate of at least 65% in the task of retrieving relevant information in the two languages considered.

show abstract

A measure theoretic approach to information retrieval

Dominich

Kiezer

2007

J. Am. Soc. Inf. Sci.

View full text Add to dashboard Cite

The vector space model of information retrieval is one of the classical and widely applied retrieval models. Paradoxically, it has been characterized by a discrepancy between its formal framework and implementable form. The underlying concepts of the vector space model are mathematical terms: linear space, vector, and inner product. However, in the vector space model, the mathematical meaning of these concepts is not preserved. They are used as mere computational constructs or metaphors. Thus, the vector space model actually does not follow formally from the mathematical concepts on which it has been claimed to rest. This problem has been recognized for more than two decades, but no proper solution has emerged so far. The present article proposes a solution to this problem. First, the concept of retrieval is defined based on the mathematical measure theory. Then, retrieval is particularized using fuzzy set theory. As a result, the retrieval function is conceived as the cardinality of the intersection of two fuzzy sets. This view makes it possible to build a connection to linear spaces. It is shown that the classical and the generalized vector space models, as well as the latent semantic indexing model, gain a correct formal background with which they are consistent. At the same time it becomes clear that the inner product is not a necessary ingredient of the vector space model, and hence of Information Retrieval (IR). The Principle of Object Invariance is introduced to handle this situation. Moreover, this view makes it possible to consistently formulate new retrieval methods: in linear space with general basis, entropy-based, and probability-based. It is also shown that Information Retrieval may be viewed as integral calculus, and thus it gains a very compact and elegant mathematical way of writing. Also, Information Retrieval may thus be conceived as an application of mathematical measure theory.

show abstract

Understanding Search Engines

Cited by 110 publications

References 0 publications

Data Mining for the Internet of Things: Literature Review and Challenges

Data Mining for the Internet of Things: Literature Review and Challenges

Cross - Language Information Retrieval Using Two Methods: LSI via SDD and LSI via SVD

A measure theoretic approach to information retrieval

Contact Info

Product

Resources

About