Titlebook: Big Data Analytics and Knowledge Discovery; 18th International C Sanjay Madria,Takahiro Hara Conference proceedings 2016 Springer Internati

Espionage

書目名稱Big Data Analytics and Knowledge Discovery影響因子(影響力)




書目名稱Big Data Analytics and Knowledge Discovery影響因子(影響力)學(xué)科排名




書目名稱Big Data Analytics and Knowledge Discovery網(wǎng)絡(luò)公開度




書目名稱Big Data Analytics and Knowledge Discovery網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Big Data Analytics and Knowledge Discovery被引頻次




書目名稱Big Data Analytics and Knowledge Discovery被引頻次學(xué)科排名




書目名稱Big Data Analytics and Knowledge Discovery年度引用




書目名稱Big Data Analytics and Knowledge Discovery年度引用學(xué)科排名




書目名稱Big Data Analytics and Knowledge Discovery讀者反饋




書目名稱Big Data Analytics and Knowledge Discovery讀者反饋學(xué)科排名

Innocence

TopPI: An Efficient Algorithm for Item-Centric Mininghe . most frequent closed itemsets that item belongs to. For example, in our retail dataset, TopPI finds the itemset “nori seaweed, wasabi, sushi rice, soy sauce” that occurrs in only 133 store receipts out of 290 million. It also finds the itemset “milk, puff pastry”, that appears 152,991 times. Th

言行自由

A Rough Connectedness Algorithm for Mining Communities in Complex Networks Though community detection is a very active research area, most of the algorithms focus on detecting disjoint community structure. However, real-world complex networks do not necessarily have disjoint community structure. Concurrent overlapping and hierarchical communities are prevalent in real-wor

Iniquitous

Mining User Trajectories from Smartphone Data Considering Data Uncertaintyh attention. Wi-Fi fingerprints are the sets of Wi-Fi scanning results recorded in mobile devices. However, the issue of data uncertainty is not considered in the proposed Wi-Fi positioning systems. In this paper, we propose a framework to find user trajectories from the Wi-Fi fingerprints recorded

強(qiáng)制性

A Heterogeneous Clustering Approach for Human Activity Recognitionormance of HAR system deployed on large-scale is often significantly lower than reported due to the sensor-, device-, and person-specific heterogeneities. In this work, we develop a new approach for clustering such heterogeneous data, represented as a time series, which incorporates different level

聰明

Hemiparesis

Mining Data Streams with Dynamic Confidence Intervalsg if its average success probability in the data stream reaches a user specified threshold. We propose an algorithm approximating the family of all interesting itemsets in a data stream. Using Chernoff bounds, our algorithm dynamically adjusts the confidence intervals of the candidate itemsets’ prob

訓(xùn)誡

Evaluating Top-K Approximate Patterns via Text Clusteringing algorithm, where the document features are derived from such patterns. Specifically, we exploit approximate patterns within the well-known . (Frequent Itemset-based Hierarchical Clustering) algorithm, which was originally designed to employ exact frequent itemsets to achieve a concise and inform

新義

拋射物

An Exhaustive Covering Approach to Parameter-Free Mining of Non-redundant Discriminative Itemsetshaustive covering, for finding non-redundant discriminative itemsets. ExCover outputs non-redundant patterns where each pattern covers best at least one positive transaction. With no control parameters limiting the search space, ExCover efficiently performs an exhaustive search for best-covering pat