Mining of Massive Datasets

By Nikolaos Korfiatis, Todor Ivanov, Sead Izberovic. 2014

1,290 citations recorded by the source corpus.

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Citations in the corpus

RUPEE: A fast and accurate purely geometric protein structure search (2018, positive): “180 In our case, the items are bags of shingle hashes for protein structures from which 181 we obtain 99 min-hashes as described in [31].”
CARER: Contextualized Affect Representations for Emotion Recognition (2018, positive): “Before using the patterns for classiﬁcation, they need to be weighted using a weighting mechanism such as tf-idf (Leskovec et al., 2014).”
A Comprehensive Survey of Clustering Algorithms (2015, unknown): “Big data shares the characteristics of 4 V’s, large in volume, rich in variety, high in velocity and doubt in veracity [133].”
GEMSEC: Graph Embedding with Self Clustering (2018, unknown): “Classical approaches to community detection depend on properties such as graph metrics, spectral properties and density of shortest paths [4].”
Verifiable Privacy-Preserving Multi-Keyword Text Search in the Cloud Supporting Similarity-Based Ranking (2013, unknown): “Inspired by the k-means method, which is the most widely used clustering technique in the data mining community [21], we use Euclidean distance (Ed) as a metric to cluster“close enough” vectors, e.”
Asymmetric Minwise Hashing (2014, unknown): “Simply sampling a random component leads to the popular LSH for hamming distance [32].”
Social media big data analytics: A survey (2019, unknown): “2) Data Locality : Data locality is the major challenge associated with di ﬀ erent types of data (Leskovec, Rajaraman, & Ullman, 2014).”
Fast, Accurate Detection of 100,000 Object Classes on a Single Machine (2013, unknown): “To do so, we employ a multi-band LSH-style hash table to reconstruct the dot products that have a significant number of hash matches [15].”
CSI: A Hybrid Deep Model for Fake News Detection (2017, unknown): “In line with existing literature on information retrieval and recommender systems [21], we construct the binary incidence matrix of which articles a user”
Learning to Hash for Indexing Big Data—A Survey (2015, unknown): “By converting rank lists to a triplet tensor matrix, listwise hashing is designed to preserve the ranking in the Hamming space [76].”
Single cell transcriptomes from human kidneys reveal the cellular identity of renal tumors (2018, unknown): “S1 to S19 Tables S1 to S12 References (10–46) Data S1 1 February 2018; accepted 2 July 2018 10.1126/science.aat1699 Young et al., Science 361, 594–599 (2018) 10 August 2018 6 of 6”
Fast Memory-efficient Anomaly Detection in Streaming Heterogeneous Graphs (2016, unknown): “In practice, the random projection vectors rl’s remain sufficiently random when each of their |S| elements are drawn uniformly from {+1,−1} instead of from a |S|-dimensional G ussian di tribution [31].”
Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks (2019, unknown): “Then the item with the closest embedding can be returned in near-constant time by using Locality Sensitive Hashing (LSH) techniques [27].”
Autoregressive Entity Retrieval (2020, unknown): “, 2019), DPR+BART, tf-idf (Leskovec et al., 2014), RAG (Lewis et al.”