Density of Closed Balls in Real-Valued and Autometrized Boolean Spaces for Clustering Applications

González, Cleotilde; Bonventi, Waldemar; Rodrigues, Andréa Lucia Braga Vieira

doi:10.1007/978-3-540-88190-2_7

Cited by 17 publications

(13 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Bray-Curtis similarity measure [14] which is sensitive to outlying values is a city-block metric. The Hamming distance [15,16] is the number of positions at which the associated symbols are different. IT-Sim (information-theoretic measure) for document similarity, proposed in [17,18].…”

Section: Related Workmentioning

confidence: 99%

Improved sqrt-cosine similarity measurement

Sohangir

Wang

2017

J Big Data

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

Improved sqrt-cosine similarity measurement

Sohangir

Wang

2017

J Big Data

View full text Add to dashboard Cite

“…Cosine similarity [3] is a measure which takes cosine of the angle between two given document vectors. The Jaccard coefficient [4] is a statistic used for comparing the similarity of two document sets. It is defined as size of intersection divided by size of union on sample data sets.…”

Section: Literature Surveymentioning

confidence: 99%

“…Pairwise-adaptive similarity [7] is a measure which selects a number of features dynamically, out of document d1 and document d2. In [4], [8] Hamming distance is used, hamming distance between two document vectors is number of positions where the corresponding symbols differ. In [9] a non-symmetric similarity measure called Kullback-Leibler divergence is described.…”

Section: Literature Surveymentioning

confidence: 99%

A Fuzzy Approach for Text Mining

Patil¹,

Dongre²

2015

IJMSC

View full text Add to dashboard Cite

Document clustering is an integral and important part of text mining. There are two types of clustering, namely, hard clustering and soft clustering. In case of hard clustering, data item belongs to only one cluster whereas in soft clustering, data point may fall into more than one cluster. Thus, soft clustering leads to fuzzy clustering wherein each data point is associated with a membership function that expresses the degree to which individual data points belong to the cluster. Accuracy is desired in information retrieval, which can be achieved by fuzzy clustering. In the work presented here, a fuzzy approach for text classification is used to classify the documents into appropriate clusters using Fuzzy C Means (FCM) clustering algorithm. Enron email dataset is used for experimental purpose. Using FCM clustering algorithm, emails are classified into different clusters. The results obtained are compared with the output produced by k means clustering algorithm. The comparative study showed that the fuzzy clusters are more appropriate than hard clusters.

show abstract

“…The cosine similarity is unable to provide information on the magnitude of differences. The Jaccard coefficient [21] is a statistic used for comparing the relationship of two sample sets, and is defined as the range of the intersection separated by the range of the combination of the sample sets. Euclidean distance [10] is a recognized similarity metric taken from Euclidean geometry field.…”

Section: Similarity Measurementioning

confidence: 99%

Evaluation of Clustering around Weighted Prototype and Genetic Algorithm for Document Categorization

Jain¹,

Shrivastava²

2015

IJCA

View full text Add to dashboard Cite

Document clustering is very important in the field of text categorization. Genetic algorithm, which is an optimization based technique which can be applied for finding out the best cluster centres easily by computing fitness values of data points. While clustering around weighted prototype technique is especially helpful when proper pairwise similarities are available. This technique does not find global solution of the objective function. Experimental result shows that F-measure and Normalized mutual information of genetic algorithm is better than clustering around weighted prototype for 20 Newsgroup dataset. F-measure and accuracy of genetic algorithm is better than clustering around weighted prototype for the Reuter-21578 dataset.

show abstract

Density of Closed Balls in Real-Valued and Autometrized Boolean Spaces for Clustering Applications

Cited by 17 publications

References 8 publications

Improved sqrt-cosine similarity measurement

Improved sqrt-cosine similarity measurement

A Fuzzy Approach for Text Mining

Evaluation of Clustering around Weighted Prototype and Genetic Algorithm for Document Categorization

Contact Info

Product

Resources

About