Algorithms for hierarchical clustering: an overview
F Murtagh, P Contreras - Wiley Interdisciplinary Reviews: Data …, 2012 - Wiley Online Library
We survey agglomerative hierarchical clustering algorithms and discuss efficient
implementations that are available in R and other software environments. We look at …
implementations that are available in R and other software environments. We look at …
Analysis of agriculture data using data mining techniques: application of big data
J Majumdar, S Naraseeyappa, S Ankalaki - Journal of Big data, 2017 - Springer
In agriculture sector where farmers and agribusinesses have to make innumerable
decisions every day and intricate complexities involves the various factors influencing them …
decisions every day and intricate complexities involves the various factors influencing them …
Recovering the number of clusters in data sets with noise features using feature rescaling factors
RC De Amorim, C Hennig - Information sciences, 2015 - Elsevier
In this paper we introduce three methods for re-scaling data sets aiming at improving the
likelihood of clustering validity indexes to return the true number of spherical Gaussian …
likelihood of clustering validity indexes to return the true number of spherical Gaussian …
[图书][B] Encyclopedia of machine learning
This comprehensive encyclopedia, with over 250 entries in an AZ format, provides easy
access to relevant information for those seeking entry into any aspect within the broad field …
access to relevant information for those seeking entry into any aspect within the broad field …
[HTML][HTML] Decision-tree, rule-based, and random forest classification of high-resolution multispectral imagery for wetland mapping and inventory
Efforts are increasingly being made to classify the world's wetland resources, an important
ecosystem and habitat that is diminishing in abundance. There are multiple remote sensing …
ecosystem and habitat that is diminishing in abundance. There are multiple remote sensing …
An overview on clustering methods
TS Madhulatha - arXiv preprint arXiv:1205.1117, 2012 - arxiv.org
Clustering is a common technique for statistical data analysis, which is used in many fields,
including machine learning, data mining, pattern recognition, image analysis and …
including machine learning, data mining, pattern recognition, image analysis and …
[图书][B] Data Mining: Concepts, models and techniques
F Gorunescu - 2011 - books.google.com
The knowledge discovery process is as old as Homo sapiens. Until some time ago this
process was solely based on the 'natural personal'computer provided by Mother Nature …
process was solely based on the 'natural personal'computer provided by Mother Nature …
[PDF][PDF] SenticNet 4: A semantic resource for sentiment analysis based on conceptual primitives
An important difference between traditional AI systems and human intelligence is the human
ability to harness commonsense knowledge gleaned from a lifetime of learning and …
ability to harness commonsense knowledge gleaned from a lifetime of learning and …
Mining heterogeneous information networks: a structural analysis approach
Most objects and data in the real world are of multiple types, interconnected, forming
complex, heterogeneous but often semi-structured information networks. However, most …
complex, heterogeneous but often semi-structured information networks. However, most …
SenticNet 3: a common and common-sense knowledge base for cognition-driven sentiment analysis
E Cambria, D Olsher, D Rajagopal - … of the AAAI conference on artificial …, 2014 - ojs.aaai.org
SenticNet is a publicly available semantic and affective resource for concept-level sentiment
analysis. Rather than using graph-mining and dimensionality-reduction techniques …
analysis. Rather than using graph-mining and dimensionality-reduction techniques …