Advanced Data Mining and Applications: 12th International by Jinyan Li, Xue Li, Shuliang Wang, Jianxin Li, Quan Z. Sheng

By Jinyan Li, Xue Li, Shuliang Wang, Jianxin Li, Quan Z. Sheng

This publication constitutes the complaints of the twelfth overseas convention on complicated facts Mining and functions, ADMA 2016, held in Gold Coast, Australia, in December 2016.

The 70 papers offered during this quantity have been rigorously reviewed and chosen from a hundred and five submissions. the chosen papers coated a wide selection of vital issues within the zone of information mining, together with parallel and dispensed facts mining algorithms, mining on information streams, graph mining, spatial information mining, multimedia info mining, internet mining, the web of items, healthiness informatics, and biomedical facts mining.

Show description

Read Online or Download Advanced Data Mining and Applications: 12th International Conference, ADMA 2016, Gold Coast, QLD, Australia, December 12-15, 2016, Proceedings PDF

Similar storage & retrieval books

Introduction to Shannon Sampling and Interpolation Theory

Regaining unique indications reworked from analog to electronic platforms or assessing details misplaced within the approach are the basic concerns addressed by means of sampling and interpolation concept. This learn makes an attempt to appreciate, generalize and expand the cardinal sequence of Shannon sampling thought.

Semantic Web Services: Theory, Tools and Applications

The Semantic internet proposes the mark-up of content material on the net utilizing formal ontologies that constitution underlying facts for the aim of entire and conveyable computing device realizing. Semantic internet prone: thought, instruments and functions brings contributions from researchers, scientists from either and academia, and representatives from diversified groups to review, comprehend, and discover the speculation, instruments, and purposes of the semantic internet.

A Unified Framework for Video Summarization, Browsing & Retrieval: with Applications to Consumer and Surveillance Video

Huge volumes of video content material can basically be simply accessed by means of fast looking and retrieval thoughts. developing a video desk of contents (ToC) and video highlights to let finish clients to sift via all this knowledge and locate what they wish, once they wish are crucial. This reference places forth a unified framework to combine those capabilities aiding effective shopping and retrieval of video content material.

Yahoo! to the Max: An Extreme Searcher Guide

Yahoo! has quite a lot of how one can locate info, speak, make investments, store, and promote, and this ebook offers an summary of the preferred internet portal. info on internet looking, discovering and customizing information, and utilizing and developing chat groups are integrated, in addition to info on utilizing Yahoo!

Extra info for Advanced Data Mining and Applications: 12th International Conference, ADMA 2016, Gold Coast, QLD, Australia, December 12-15, 2016, Proceedings

Sample text

The relative difference in the use of a feature is determined as dif f = meancomm − meanpers ∗ 100 (meancomm + meanpers )/2 (1) If dif f > 0, the feature is used more in Community than in Personal, and vice versa. 4 Classification Lasso as the Classifier and Feature Selector. Denote by B a corpus of N posts made in community or personal blogs. A document d ∈ B is denoted (d) as x(d) = [. . , xi , . ], a vector of features. The feature sets experimented in this work are topics, extracted through topic modeling (LDA) and language (d) styles (LIWC).

It appears that people could freely post in their own pages with informal and unprepared text. Similarly, swear words were used more in personal than in community pages. It may be because posting to the communities is often gone through a moderation process. So, a post with inappropriate words could be rejected to be posted to the community pages. ly/1KEgjpM. Textual Cues for Online Depression in Community and Personal Settings 29 Table 5. Performance, in terms of the predictive accuracy (percentage of correct predictions), of different classifiers on different feature sets in the binary classifications of Community versus Personal posts.

Otherwise, the new instance is simply added to the buffer. 40 M. Khalid et al. Algorithm 1. A Framework for Confidence-Weighted Bipartite Ranking (CBR) Input: • • • • the penalty parameter C the capacity of the buffers M+ and M− η parameter ai = 1 for i ∈ 1, . . , d Initialize: μ1 = {0, . . , 0}d , B+ = B− = ∅ Σ1 = diag(a) or G1 = a for t = 1, . . 3 or (CBR-diag) or (CBR-diag) Update Ranker Inspired by the robust performance of second-order learning algorithms, we apply the soft confidence-weighted learning approach [35] to updated the bipartite ranking function.

Download PDF sample

Rated 4.21 of 5 – based on 9 votes