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Profiling US Restaurants from Billions of Payment Card Transactions [article]

Himel Dev, Hossein Hamooni
2020 arXiv   pre-print
A payment card (such as debit or credit) is one of the most convenient payment methods for purchasing goods and services. Hundreds of millions of card transactions take place across the globe every day, generating a massive volume of transaction data. The data render a holistic view of cardholder-merchant interactions, containing insights that can benefit various applications, such as payment fraud detection and merchant recommendation. However, utilizing these insights often requires
more » ... requires additional information about merchants missing from the data owner's (i.e., payment company's) perspective. For example, payment companies do not know the exact type of product a merchant serves. Collecting merchant attributes from external sources for commercial purposes can be expensive. Motivated by this limitation, we aim to infer latent merchant attributes from transaction data. As proof of concept, we concentrate on restaurants and infer the cuisine types of restaurants from transactions. To this end, we present a framework for inferring the cuisine types of restaurants from transaction data. Our proposed framework consists of three steps. In the first step, we generate cuisine labels for a limited number of restaurants via weak supervision. In the second step, we extract a wide variety of statistical features and neural embeddings from the restaurant transactions. In the third step, we use deep neural networks (DNNs) to infer the remaining restaurants' cuisine types. The proposed framework achieved a 76.2% accuracy in classifying the US restaurants. To the best of our knowledge, this is the first framework to infer the cuisine types of restaurants by analyzing transaction data as the only source.
arXiv:2009.02461v1 fatcat:hvydu75n3be2znczuqcpqllala

Dual-Domain Hierarchical Classification of Phonetic Time Series

Hossein Hamooni, Abdullah Mueen
2014 2014 IEEE International Conference on Data Mining  
Phonemes are the smallest units of sound produced by a human being. Automatic classification of phonemes is a well-researched topic in linguistics due to its potential for robust speech recognition. With the recent advancement of phonetic segmentation algorithms, it is now possible to generate datasets of millions of phonemes automatically. Phoneme classification on such datasets is a challenging data mining task because of the large number of classes (over a hundred) and complexities of the
more » ... plexities of the existing methods. In this paper, we introduce the phoneme classification problem as a data mining task. We propose a dual-domain (time and frequency) hierarchical classification algorithm. Our method uses a Dynamic Time Warping (DTW) based classifier in the top layers and time-frequency features in the lower layer. We crossvalidate our method on phonemes from three online dictionaries and achieved up to 35% improvement in classification compared to existing techniques. We provide case studies on classifying accented phonemes and speaker invariant phoneme classification.
doi:10.1109/icdm.2014.92 dblp:conf/icdm/HamooniM14 fatcat:5yep2wnvp5curbai7j64iwekga

Time Series Join on Subsequence Correlation

Abdullah Mueen, Hossein Hamooni, Trilce Estrada
2014 2014 IEEE International Conference on Data Mining  
We consider the problem of joining two long time series based on their most correlated segments. Two time series can be joined at any locations and for arbitrary length. Such join locations and length provide useful knowledge about the synchrony of the two time series and have applications in many domains including environmental monitoring, patient monitoring and power monitoring. However, join on correlation is a computationally expensive task, specially when the time series are large. The
more » ... are large. The naive algorithm requires O(n 4 ) computation where n is the length of the time series. We propose an algorithm, named Jocor, that uses two algorithmic techniques to tackle the complexity. First, the algorithm reuses the computation by caching sufficient statistics and second, the algorithm prunes unnecessary correlation computation by admissible heuristics. The algorithm runs orders of magnitude faster than the naive algorithm and enables us to join long time series as well as many small time series. We propose a variant of Jocor for fast approximation and an extension to a GPU-based parallel method to bring down the running-time to interactive level for analytics applications. We show three independent uses of time series join on correlation which are made possible by our algorithm.
doi:10.1109/icdm.2014.52 dblp:conf/icdm/MueenHE14 fatcat:3f3btewqczfuliwzymxhszczti

Temporal Patterns in Bot Activities

Nikan Chavoshi, Hossein Hamooni, Abdullah Mueen
2017 Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion  
Correlated or synchronized bots commonly exist in social media sites such as Twitter. Bots work towards gaining human followers, participating in campaigns, and engaging in unethical activities such as spamming and false click generation. In this paper, we perform temporal pattern mining on bot activities in Twitter. We discover motifs (repeating behavior), discords (anomalous behavior), joins, bursts and dynamic clusters in activities of Twitter bots, and explain the significance of these
more » ... cance of these temporal patterns in gaining competitive advantage over humans. Our analysis identifies a small set of indicators that separates bots from humans with high precision. Random 102f2kid, 10 vivid, 125gn3a6, 142d4afaf, 17E4a3fb, 18Hghhgjgjd, 19Kytghgfdd, 1oTalalaykina, 229ae, 22Tjdtjtgkytk, 25Gfhthysjtj, 26Gjghtrhysxrf, 28 ghjtrjtyjtgh, 2Asagao543210, 2Ic65ec, 2ch33n5, 2gbm8p7, 2hgddg, 2j8p3ab, 33634m87, 37Hkyjdtytyhjgh, 3Vistlip, 3bf72, 3en2p, 5Asagao543210, 5Mbityutskikh, 6Asagao543210, 7759c5, 79 shamilya, 7Asagao543210, 9F6m36 East European 2016Kuramshina, 4uOkaderkaev, 9 chitanova90, Bravkov73K,
doi:10.1145/3041021.3051114 dblp:conf/www/ChavoshiHM17a fatcat:eaasl37dkfadfmbycvb73r5u2a

AWarp: Fast Warping Distance for Sparse Time Series

Abdullah Mueen, Nikan Chavoshi, Noor Abu-El-Rub, Hossein Hamooni, Amanda Minnich
2016 2016 IEEE 16th International Conference on Data Mining (ICDM)  
Dynamic Time Warping (DTW) distance has been effectively used in mining time series data in a multitude of domains. However, in its original formulation DTW is extremely inefficient in comparing long sparse time series, containing mostly zeros and some unevenly spaced non-zero observations. Original DTW distance does not take advantage of this sparsity, leading to redundant calculations and a prohibitively large computational cost for long time series. We derive a new time warping similarity
more » ... rping similarity measure (AWarp) for sparse time series that works on the run-length encoded representation of sparse time series. The complexity of AWarp is quadratic on the number of observations as opposed to the range of time of the time series. Therefore, AWarp can be several orders of magnitude faster than DTW on sparse time series. AWarp is exact for binary-valued time series and a close approximation of the original DTW distance for any-valued series. We discuss useful variants of AWarp: bounded (both upper and lower), constrained, and multidimensional. We show applications of AWarp to three data mining tasks including clustering, classification, and outlier detection, which are otherwise not feasible using classic DTW, while producing equivalent results. Potential areas of application include bot detection, human activity classification, and unusual review pattern mining.
doi:10.1109/icdm.2016.0046 dblp:conf/icdm/MueenCAHM16 fatcat:smpqztvq2ve5jh5u7g4z4uwtv4

The effects of intravenous hydration on amniotic fluid volume and pregnancy outcomes in women with term pregnancy and oligohydramnios: a randomized clinical trial

Mahnaz Shahnazi, Manizheh Sayyah Meli, Fariba Hamoony, Farnaz Sadrimehr, Fatemeh Ghatre Samani, Hossein Koshavar
2012 Journal of Caring Sciences  
Amniotic fluid is an important factor in the prediction of fetal survival. The aim of this research was to evaluate the effects of intravenous hydration of mothers on amniotic fluid volume and in turn on pregnancy outcomes. The current single blind controlled clinical trial was conducted on 20 pregnant mothers with amniotic fluid index of lower or equal to 5 cm and gestational age of 37-41 weeks. The subjects were divided into two groups of case and control through simple random sampling.
more » ... dom sampling. Amniotic fluid index was measured in all participants. The case group received one liter of isotonic saline during 30 minutes by the bolus method. Reevaluations of amniotic fluid index in both groups were made 90 minutes after baseline measurement. Independent t-test and paired t-test were used to compare the two groups and mean amniotic fluid index before and after treatment, respectively. Hydration of mothers significantly increased the amniotic fluid index in the case group (mean change: 1.5 cm; 95%CI: 0.46 - 2.64; P = 0.01). The mean change of amniotic fluid index in the control group did not significantly increase (P = 0.06). The elevation of amniotic fluid index in the hydration group (32%) was significantly higher than the control group (1%) (P = 0.03). In this study intravenous hydration increased amniotic fluid index of mothers with term pregnancy and oligohydramnios. Since it caused no complications for the mother and the fetus, it can be used as an effective method in management of oligohydramnios.
doi:10.5681/jcs.2012.018 pmid:25276686 pmcid:PMC4161079 fatcat:klw4lufhrnf5hp3pzkanvwozsm

Compilation of geology, mineralization, geochemistry and geophysical study of IP/RS & ground magnetic survey at Roudgaz area, southeast of Gonabad, Khorasan Razavi province

Hossein Hajimirzajan, Mohammad Hassan Karimpour, Azadeh Malekzadeh Shafaroudi, Mohammad Reza Haidarian Shahri, Seyed Javad Hamooni
2013 Journal of Economic Geology  
Roudgaz prospect area is a Cu, Sn, Pb, Zn, and Au polymetal vein system located to the southeast of Gonabad and in the northeast of Lut block. Oxidan subvolcanic Tertiary rocks with monzonite to monzodiorite porphyry composition intruded the metamorphic rocks of middle Jurassic. The majority of intrusive bodies are affected by carbonation, argillic, sericitic, and silicification-tourmaline alteration. Mineralization in the area is controlled by fault and is present as vein with domination of
more » ... th domination of NW-SE direction and 85-90º dip. Primary minerals are quartz, tourmaline, chalcopyrite, pyrite, and secondary minerals are malachite, azurite, and goethite. Geochemical sampling using chip composite method indicated high anomalies of Cu, Sn, Pb, and As (up to 10000 ppm), Zn (up to 5527 ppm), and Au (up to 325 ppb). Broad gossan zone is present in the area and is related to the oxidation of sulfide minerals. IP/RS survey was performed over the geochemical anomalies for identification of the location and extension of sulfide mineralization at depth. Generally, chargeability increases in gossan zones, veins, old workings and geochemical anomalies. Resistivity over the quartzite unit and also in locations where mineralized vein is associated with quartz has a high anomaly of up to 425 ohm-m. Due to high geochemical anomaly of Sn and its relation with reduced subvolcanic intrusives, ground magnetic survey was performed to identify the location of magnetite (oxidant) and ilmenite (reduced) series at depth. Variation of Total Magnetic Intensity (TMI) is 335.1 Gamma in the TMI map. The highest magnetic anomalies in the RTP map are located to the north of the survey area which is related to magnetite series (hornblende biotite monzodiorite porphyry) and extend to the south at depth. The lowest magnetic anomaly is located to the center of the survey area and particularly to the east of the Roudgaz village correlating the highest chargeability and geochemical anomaly. Based on coincidence of geochemical and chargeability anomalies, two h [...]
doaj:ce1485feba5d4284959cef505b1100c4 fatcat:ndca7ipxqffddpp724ix47fgeq