標(biāo)題: Titlebook: Data Mining for Service; Katsutoshi Yada Book 2014 Springer-Verlag Berlin Heidelberg 2014 Data Mining.Domain Knowledge.Large Database.Sens [打印本頁] 作者: 生長變吼叫 時(shí)間: 2025-3-21 19:59
書目名稱Data Mining for Service影響因子(影響力)
書目名稱Data Mining for Service影響因子(影響力)學(xué)科排名
書目名稱Data Mining for Service網(wǎng)絡(luò)公開度
書目名稱Data Mining for Service網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Data Mining for Service被引頻次
書目名稱Data Mining for Service被引頻次學(xué)科排名
書目名稱Data Mining for Service年度引用
書目名稱Data Mining for Service年度引用學(xué)科排名
書目名稱Data Mining for Service讀者反饋
書目名稱Data Mining for Service讀者反饋學(xué)科排名
作者: intellect 時(shí)間: 2025-3-21 20:28 作者: Needlework 時(shí)間: 2025-3-22 03:15
Paul van de Laar,Arie van der Schoorl evaluation on two benchmark corpora. These experiments indicate that our algorithm can deliver a substantial reduction in the number of features, from 8,742 to 500 and from 47,236 to 392 features, while preserving or even improving the retrieval performance.作者: 陳腐思想 時(shí)間: 2025-3-22 08:20
Commentar zur Pharmacopoea Germanicar different sources. The system we proposed here is the Multi-Collaborative Filtering Trust Network Recommendation System, which combined multiple online sources, measured trust, temporal relation and similarity factors.作者: 完成 時(shí)間: 2025-3-22 11:15 作者: dilute 時(shí)間: 2025-3-22 15:15
Dimensionality Reduction for Information Retrieval Using Vector Replacement of Rare Termsl evaluation on two benchmark corpora. These experiments indicate that our algorithm can deliver a substantial reduction in the number of features, from 8,742 to 500 and from 47,236 to 392 features, while preserving or even improving the retrieval performance.作者: dilute 時(shí)間: 2025-3-22 17:43 作者: 打擊 時(shí)間: 2025-3-22 21:40 作者: 爵士樂 時(shí)間: 2025-3-23 01:55
Scam Detection in Twittere and Bayes Information Criteria is investigated and combined with the classification step. Our experiments show that 87?% accuracy is achievable with only 9 labeled samples and 4000 unlabeled samples, among other results.作者: Axon895 時(shí)間: 2025-3-23 09:27
Change Detection from Heterogeneous Data Sourcese describe an approach of singular spectrum transformation for change-point detection for heterogeneous data. We also introduce a novel technique of proximity-based outlier detection to handle the dynamic nature of the data. Using real-world sensor data, we demonstrate the utility of the proposed methods.作者: ACRID 時(shí)間: 2025-3-23 10:01 作者: Fsh238 時(shí)間: 2025-3-23 17:01 作者: RAG 時(shí)間: 2025-3-23 20:23 作者: 使熄滅 時(shí)間: 2025-3-24 02:13 作者: overwrought 時(shí)間: 2025-3-24 06:20 作者: nonchalance 時(shí)間: 2025-3-24 08:22
Feature Selection Over Distributed Data Streams function over distributed data streams through a set of constraints applied separately on each stream. We report numerical experiments on a real–world data that detect instances where communication between nodes is required, and compare the approach and the results to those recently reported in the literature.作者: periodontitis 時(shí)間: 2025-3-24 11:28
Learning Hidden Markov Models Using Probabilistic Matrix Factorizationved sequence. We compare the Baum–Welch with the proposed algorithm in various experimental settings and present empirical evidences of the benefits of the proposed method in regards to the reduced time complexity and increased robustness.作者: 慢慢啃 時(shí)間: 2025-3-24 16:15 作者: 不開心 時(shí)間: 2025-3-24 19:36 作者: 孤僻 時(shí)間: 2025-3-25 03:02 作者: Crater 時(shí)間: 2025-3-25 05:32
Studies in Big Datahttp://image.papertrans.cn/d/image/262952.jpg作者: Indicative 時(shí)間: 2025-3-25 07:31 作者: 定點(diǎn) 時(shí)間: 2025-3-25 14:46 作者: LATE 時(shí)間: 2025-3-25 17:14 作者: sed-rate 時(shí)間: 2025-3-25 22:18 作者: 機(jī)警 時(shí)間: 2025-3-26 03:12 作者: indifferent 時(shí)間: 2025-3-26 07:50
Paul van de Laar,Arie van der Schoore introduce a new approach to dimensionality reduction for text retrieval. According to Zipf’s law, the majority of indexing terms occurs only in a small number of documents. Our new algorithm exploits this observation to compute a dimensionality reduction. It replaces rare terms by computing a vect作者: 熱情贊揚(yáng) 時(shí)間: 2025-3-26 08:54
Comment tremble la main invisibleanel data sets frequently appeared in the study of Marketing, Economics, and many other social sciences. An important panel data analysis task is to analyze and predict a variable of interest. As in social sciences, the number of collected data records for each subject is usually not large enough to作者: 生氣的邊緣 時(shí)間: 2025-3-26 15:58
Commentar zur Pharmacopoea Germanica that can be predicted from the text of publication abstracts from those, for successes in prediction are spurious. The significance of relationships between textual data and information that is represented in standardized ontologies and protein domains is evaluated using a density-based approach. T作者: GEON 時(shí)間: 2025-3-26 17:54 作者: Prostatism 時(shí)間: 2025-3-26 22:36
https://doi.org/10.1007/978-3-642-51833-1 research, we extend the text mining system for the call summary records and construct a conversation mining system for the business-oriented conversation at the contact center. To acquire useful business insights from the conversation data through the text mining system, it is critical to identify 作者: Interlocking 時(shí)間: 2025-3-27 04:57
Commentar zur Pharmacopoea Germanica There is relatively little effort in identifying scams in Twitter. In this chapter, we propose a semi-supervised Twitter scam detector based on a small labeled data. The scam detector combines self-learning and clustering analysis. A suffix tree data structure is used. Model building based on Akaik作者: Cryptic 時(shí)間: 2025-3-27 05:56
Commentar zur Pharmacopoea Germanicais mainly limited to analyzing a single data source. In this chapter, we propose a novel joint matrix factorization framework which can jointly analyze multiple data sources by exploiting their shared and individual structures. The proposed framework is flexible to handle any arbitrary sharing confi作者: Forage飼料 時(shí)間: 2025-3-27 13:08
Commentar zur Pharmacopoea Germanica networks by filtering information and offering useful recommendations to customers. Collaborative Filtering (CF) is believed to be a suitable underlying technique for recommendation systems based on social networks, and social networks provide the needed collaborative social environment. CF and its作者: 不連貫 時(shí)間: 2025-3-27 14:07
Commentar zur Pharmacopoea Germanicaer classes. Additionally, in many real-life problem domains, data with an imbalanced class distribution contains ambiguous regions in the data space where the prior probability of two or more classes are approximately equal. This problem, known as overlapping classes, thus makes it difficult for the作者: faction 時(shí)間: 2025-3-27 19:27
https://doi.org/10.1007/978-3-642-51833-1ituation is an important technical challenge. In this chapter, we focus on . technologies, including the tasks of outlier detection and change-point detection. In particular, we focus on how to handle the heterogeneous and dynamic natures that are common features of the data in service businesses. W作者: exceed 時(shí)間: 2025-3-27 22:43 作者: Exuberance 時(shí)間: 2025-3-28 02:50 作者: 先行 時(shí)間: 2025-3-28 06:54
https://doi.org/10.1007/978-3-642-45252-9Data Mining; Domain Knowledge; Large Database; Sensor Network; Social Media; Strategic Use of Data作者: Terrace 時(shí)間: 2025-3-28 11:21 作者: 安慰 時(shí)間: 2025-3-28 16:55
Panel Data Analysis Via Variable Selection and Subject Clusteringble of interest. A regression on many irrelevant regressors will lead to wrong predictions. To address these two issues, we propose a novel approach, called ., which derives underlying linear models by first selecting variables highly correlated to the variable of interest and then clustering subjec作者: Analogy 時(shí)間: 2025-3-28 19:11 作者: neutralize 時(shí)間: 2025-3-29 02:38
Text Mining of Business-Oriented Conversations at a Call Centerngthy and redundant. In this research, we define the model of the business-oriented conversations and propose a mining method to identify segments that make impact on the outcome of the conversation and extract useful expressions in each identified segments. In the experiment, we process the real da作者: 擺動(dòng) 時(shí)間: 2025-3-29 03:38
A Matrix Factorization Framework for Jointly Analyzing Multiple Nonnegative Data Sourcesrieval performance exceeds the existing state-of-the-art techniques. The proposed solution provides a generic framework and can be applicable to a wider context in data mining wherever one needs to exploit mutual and individual knowledge present across multiple data sources.作者: 果仁 時(shí)間: 2025-3-29 10:50 作者: 現(xiàn)任者 時(shí)間: 2025-3-29 14:04
Text Document Cluster Analysis Through Visualization of 3D Projectionsant to generate a display (or users may choose any three orthogonal axes). We conducted implementation studies to demonstrate the value of our system with an artificial data set and a de facto benchmark news article dataset from the United States NIST Text REtrieval Competitions (TREC).作者: Congeal 時(shí)間: 2025-3-29 17:09 作者: alcoholism 時(shí)間: 2025-3-29 22:35 作者: 桶去微染 時(shí)間: 2025-3-30 00:40
https://doi.org/10.1007/978-3-642-51833-1with terms such as activation, interferon, cell and signaling. Further examination of several clusters, using gene pathway databases as well as natural language processing tools, revealed that nonnegative tensor factorization accurately identified genes and TFs in well established signaling pathways作者: 愛好 時(shí)間: 2025-3-30 07:13
https://doi.org/10.1007/978-3-642-51833-1ngthy and redundant. In this research, we define the model of the business-oriented conversations and propose a mining method to identify segments that make impact on the outcome of the conversation and extract useful expressions in each identified segments. In the experiment, we process the real da作者: 小樣他閑聊 時(shí)間: 2025-3-30 11:47 作者: ethereal 時(shí)間: 2025-3-30 14:40 作者: 巨大沒有 時(shí)間: 2025-3-30 19:14
Commentar zur Pharmacopoea Germanicaant to generate a display (or users may choose any three orthogonal axes). We conducted implementation studies to demonstrate the value of our system with an artificial data set and a de facto benchmark news article dataset from the United States NIST Text REtrieval Competitions (TREC).作者: Dungeon 時(shí)間: 2025-3-30 22:44 作者: 一再煩擾 時(shí)間: 2025-3-31 02:19
Data Mining for Servicereasingly important in various fields [., .]. In developed countries, service industries comprise a very high percentage of GDP, and even in manufacturing, in order to gain a competitive advantage, there is a focus on services which create added value.作者: 公司 時(shí)間: 2025-3-31 07:12
Feature Selection Over Distributed Data Streamsg the information gain of various features) requires a very high communication overhead when addressed using straightforward centralized algorithms. While most of the existing algorithms deal with monitoring simple aggregated values such as frequency of occurrence of stream items, motivated by recen作者: 陳腐思想 時(shí)間: 2025-3-31 10:22
Learning Hidden Markov Models Using Probabilistic Matrix Factorizationrameters of a HMM are estimated using the Baum–Welch algorithm, which scales linearly with the sequence length and quadratically with the number of hidden states. In this chapter, we propose a significantly faster algorithm for HMM parameter estimation. The crux of the algorithm is the probabilistic作者: insightful 時(shí)間: 2025-3-31 16:22
Dimensionality Reduction for Information Retrieval Using Vector Replacement of Rare Termse introduce a new approach to dimensionality reduction for text retrieval. According to Zipf’s law, the majority of indexing terms occurs only in a small number of documents. Our new algorithm exploits this observation to compute a dimensionality reduction. It replaces rare terms by computing a vect
