By Vincent S. Tseng, Tu Bao Ho, Zhi-Hua Zhou, Arbee L.P. Chen, Hung-Yu Kao

The two-volume set LNAI 8443 + LNAI 8444 constitutes the refereed lawsuits of the 18th Pacific-Asia convention on wisdom Discovery and information Mining, PAKDD 2014, held in Tainan, Taiwan, in may well 2014. The forty complete papers and the 60 brief papers awarded inside those court cases have been rigorously reviewed and chosen from 371 submissions. They disguise the overall fields of development mining; social community and social media; category; graph and community mining; functions; privateness conserving; advice; function choice and aid; computer studying; temporal and spatial facts; novel algorithms; clustering; biomedical facts mining; movement mining; outlier and anomaly detection; multi-sources mining; and unstructured info and textual content mining.

Show description

Read or Download Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014. Proceedings, Part I PDF

Similar data mining books

Data Visualization: Part 1, New Directions for Evaluation, Number 139

Do you converse information and knowledge to stakeholders? This factor is a component 1 of a two-part sequence on info visualization and overview. partially 1, we introduce fresh advancements within the quantitative and qualitative facts visualization box and supply a historic viewpoint on facts visualization, its strength function in evaluate perform, and destiny instructions.

Big Data Imperatives: Enterprise Big Data Warehouse, BI Implementations and Analytics

Huge facts Imperatives, makes a speciality of resolving the foremost questions about everyone’s brain: Which facts concerns? Do you've gotten sufficient information quantity to justify the utilization? the way you are looking to strategy this quantity of information? How lengthy do you really want to maintain it energetic in your research, advertising and marketing, and BI purposes?

Learning Analytics in R with SNA, LSA, and MPIA

This e-book introduces significant Purposive interplay research (MPIA) idea, which mixes social community research (SNA) with latent semantic research (LSA) to aid create and examine a significant studying panorama from the electronic strains left via a studying group within the co-construction of data.

Metadata and Semantics Research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings

This ebook constitutes the refereed court cases of the tenth Metadata and Semantics examine convention, MTSR 2016, held in Göttingen, Germany, in November 2016. The 26 complete papers and six brief papers awarded have been rigorously reviewed and chosen from sixty seven submissions. The papers are equipped in numerous periods and tracks: electronic Libraries, details Retrieval, associated and Social facts, Metadata and Semantics for Open Repositories, study info platforms and information Infrastructures, Metadata and Semantics for Agriculture, meals and atmosphere, Metadata and Semantics for Cultural Collections and purposes, ecu and nationwide initiatives.

Additional resources for Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014. Proceedings, Part I

Sample text

08 add the number of edges to increase the height of the items. The height of the simulated concept hierarchy is 12 and the distribution of items are shown in the Table 2. Table 5 provides the details of drank of UP by considering the simulated concept hierarchy. In this table, the first column shows the pattern, the second column shows the drank of UP, the third column shows the drank of UP with E, and the last column shows difference between the drank of UP and the drank of UP with E. The values in the last column show that the notion of AF , along with M F and LF , helps in computing the drank for all kinds of patterns including less unbalanced patterns to high unbalanced patterns.

75, 1, and 1 respectively. (iii) Computing the drank of UP The drank of UP is a function of M F , AF and LF . Definition 7. Diverse rank of a frequent pattern Y (drank(Y)): Let Y be the pattern and U be the unbalanced concept hierarchy of height ‘h’. The drank of Y , denoted by drank(Y ), is given by the following equation. l=0 [M F (Y, l, P (Y /E)) ∗ AF (Y, l, P (Y /E))] ∗ LF (l, P (Y /E)) drank(Y, U ) = l=h−1 (5) where, h is the height of the P (P/E), E is the extended unbalanced concept hierarchy, M F (Y, l, P (Y /E)) is the M F of Y at level l, LF (l, P (Y /E)) is the LF at level l and AF (Y, l, P (Y /E)) is the AF of Y at level l.

7 is used for supporting clustering algorithms. 2 Analysis of the Honeynet Data For this dataset, we set R = 5 as the low rank of the tensor decomposition; after decomposing the tensor, we obtain three factor matrices each representing one of the three modes of our data: source IP, target IP and timestamp respectively. Each column of those factor matrices corresponds to one out of the R latent groups, in our low rank representation of the data. Based on this low rank embedding of the data, we compare pairs of columns for each factor matrix, in order to detect outliers.

Download PDF sample

Rated 4.29 of 5 – based on 24 votes