標(biāo)題: Titlebook: Big Data Analytics and Knowledge Discovery; 18th International C Sanjay Madria,Takahiro Hara Conference proceedings 2016 Springer Internati [打印本頁] 作者: Espionage 時(shí)間: 2025-3-21 17:43
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
作者: Innocence 時(shí)間: 2025-3-21 21:00
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作者: 言行自由 時(shí)間: 2025-3-22 01:07
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作者: Iniquitous 時(shí)間: 2025-3-22 06:10
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 作者: 強(qiáng)制性 時(shí)間: 2025-3-22 11:29
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 作者: 聰明 時(shí)間: 2025-3-22 14:34 作者: Hemiparesis 時(shí)間: 2025-3-22 19:11
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作者: 訓(xùn)誡 時(shí)間: 2025-3-23 00:36
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作者: 新義 時(shí)間: 2025-3-23 04:12 作者: 拋射物 時(shí)間: 2025-3-23 08:57
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作者: 耕種 時(shí)間: 2025-3-23 13:02 作者: machination 時(shí)間: 2025-3-23 14:37
Power of Bosom Friends, POI Recommendation by Learning Preference of Close Friends and Similar Userssearches on social networks, such as POI (point of interest) recommendation, usually ignore the social tie strength between users. If we can further consider the closeness between friends in the analysis, it is possible to improve the results. Therefore, in this paper, we focus on analyzing the soci作者: irreparable 時(shí)間: 2025-3-23 18:52 作者: Diluge 時(shí)間: 2025-3-24 01:17
Large Scale Indexing and Searching Deep Convolutional Neural Network Featuresp Convolutional Neural Network Features to support efficient retrieval on very large image databases. The idea is to provide a text encoding for these features enabling the use of a text retrieval engine to perform image similarity search. In this way, we built . a robust retrieval system that combi作者: 未成熟 時(shí)間: 2025-3-24 02:29
Conference proceedings 2016zed in topical sections on Mining Big Data, Applications of Big Data Mining, Big Data Indexing and Searching, Big Data Learning and Security, Graph Databases and Data Warehousing, Data Intelligence and Technology..作者: 我不重要 時(shí)間: 2025-3-24 09:02 作者: Inscrutable 時(shí)間: 2025-3-24 13:14
Corpus-Based Study of Translation Practice,terns using branch-and-bound pruning. During the search, candidate best-covering patterns are concurrently collected for each positive transaction. Formal discussions and experimental results exhibit that ExCover efficiently finds a more compact set of patterns in comparison with previous methods.作者: 特別容易碎 時(shí)間: 2025-3-24 14:55 作者: Irritate 時(shí)間: 2025-3-24 19:09
https://doi.org/10.1007/978-3-8350-9231-0rocess. The developed RUP algorithm can recursively discover recent HUPs; the computational cost and memory usage can be greatly reduced without candidate generation. Several pruning strategies are also designed to speed up the computation and reduce the search space for mining the required information.作者: ANT 時(shí)間: 2025-3-24 23:56
https://doi.org/10.1007/978-3-8350-9231-0lgorithm on probabilistic sequential pattern mining is used for finding user trajectories. A series of experiments are performed to evaluate each step of the framework. The experiment results reveal that each step of our framework is with high accuracy.作者: 點(diǎn)燃 時(shí)間: 2025-3-25 04:34 作者: 浮夸 時(shí)間: 2025-3-25 09:33
Corpus-Based Study of Translational Norms,are not necessarily independent. In fact, the experimental results show the superiority of our algorithm over state-of-the-art frequent itemset mining algorithms in data streams if high F-measure and short processing time per transaction are crucial requirements at the same time.作者: 爵士樂 時(shí)間: 2025-3-25 12:08
Introducing Corpus-based Translation Studiesstrength. Finally, the location list for POI recommendation will be constructed accordingly. Experimental results show that the proposed method significantly outperforms the competitor on both precision and recall.作者: TAIN 時(shí)間: 2025-3-25 18:38 作者: 歹徒 時(shí)間: 2025-3-25 20:15 作者: 戰(zhàn)役 時(shí)間: 2025-3-26 03:06 作者: SAGE 時(shí)間: 2025-3-26 05:13 作者: 粘 時(shí)間: 2025-3-26 11:03 作者: 割讓 時(shí)間: 2025-3-26 14:42 作者: Thymus 時(shí)間: 2025-3-26 18:07
,Demand and Supply — a Brief Overview,omes a specific heterogeneity (e.g., a sensor-specific heterogeneity). Experimental evaluation on Electromyography (EMG) sensor dataset with heterogeneities shows that our method performs favourably compared to other time series clustering approaches.作者: 披肩 時(shí)間: 2025-3-26 23:18 作者: OTHER 時(shí)間: 2025-3-27 02:22 作者: metropolitan 時(shí)間: 2025-3-27 07:59 作者: 節(jié)省 時(shí)間: 2025-3-27 13:07 作者: 氣候 時(shí)間: 2025-3-27 16:19
Evaluating Top-K Approximate Patterns via Text Clusteringlity in extracting patterns that well characterize the document topics in terms of the quality of clustering obtained by .. Extensive and reproducible experiments, conducted on publicly available text corpora, show that approximate itemsets provide a better representation than exact ones.作者: 背書 時(shí)間: 2025-3-27 18:37 作者: faultfinder 時(shí)間: 2025-3-28 01:20 作者: libertine 時(shí)間: 2025-3-28 05:52 作者: Forehead-Lift 時(shí)間: 2025-3-28 08:35
Conference proceedings 2016Porto, Portugal, September 2016...The 25 revised full papers presented were carefully reviewed and selected from 73 submissions. The papers are organized in topical sections on Mining Big Data, Applications of Big Data Mining, Big Data Indexing and Searching, Big Data Learning and Security, Graph Da作者: Bouquet 時(shí)間: 2025-3-28 14:18
0302-9743 d Knowledge Discovery, DaWaK 2016, held in Porto, Portugal, September 2016...The 25 revised full papers presented were carefully reviewed and selected from 73 submissions. The papers are organized in topical sections on Mining Big Data, Applications of Big Data Mining, Big Data Indexing and Searchin作者: figure 時(shí)間: 2025-3-28 16:59 作者: N防腐劑 時(shí)間: 2025-3-28 19:35 作者: aptitude 時(shí)間: 2025-3-29 02:05 作者: Integrate 時(shí)間: 2025-3-29 04:51 作者: lambaste 時(shí)間: 2025-3-29 09:20
,Demand and Supply — a Brief Overview,ormance 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 作者: CURB 時(shí)間: 2025-3-29 14:41 作者: Acetabulum 時(shí)間: 2025-3-29 15:51 作者: Inscrutable 時(shí)間: 2025-3-29 21:03 作者: 使高興 時(shí)間: 2025-3-30 00:09
New Frontiers in Translation Studiesdly and timely detect in real-time these spatial clusters, algorithms explored grid-based approaches, which segments the spatial domain into discrete cells. The primary benefit of this approach is that it switches the costly distance comparison of density-based algorithms to counting the number of m作者: ventilate 時(shí)間: 2025-3-30 06:05 作者: 哺乳動(dòng)物 時(shí)間: 2025-3-30 09:35
Corpus-Based Study of Translation Teaching,lter non-similar pairs in an early stage. At first, for each data point, the dimension with the maximum value is used to decide the corresponding segment of data partition. An adjusting method is designed to balance the number of elements in each data segment. The similar pairs consist of inter-segm作者: BUDGE 時(shí)間: 2025-3-30 15:49
Introducing Corpus-based Translation Studiessearches on social networks, such as POI (point of interest) recommendation, usually ignore the social tie strength between users. If we can further consider the closeness between friends in the analysis, it is possible to improve the results. Therefore, in this paper, we focus on analyzing the soci作者: Sinus-Rhythm 時(shí)間: 2025-3-30 17:41 作者: Connotation 時(shí)間: 2025-3-30 21:13 作者: 撤退 時(shí)間: 2025-3-31 01:19 作者: Gnrh670 時(shí)間: 2025-3-31 06:20
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/185608.jpg作者: CRATE 時(shí)間: 2025-3-31 11:46
