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Twitter volume spikes

Yuexin Mao, Wei Wei, Bing Wang
2013 Proceedings of the 7th Workshop on Social Network Mining and Analysis - SNAKDD '13  
Simulation over a half year's stock market data indicates that it achieves on average 8.6% gain in 27 trading days and 15.0% gain in 55 trading days.  ...  Through correlation analysis, we provide insight on when Twitter volume spikes occur and possible causes of these spikes.  ...  The stock of MHFI is purchased twice, on 2/13/13 and 11/8/12.  ... 
doi:10.1145/2501025.2501039 dblp:conf/kdd/MaoWW13 fatcat:bwvqoe3pajdqno6oladamh2apy

An Empirical Research on Spatial Data Mining

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
A lot of effort put forth to summarize various spatial based knowledge discovery in data mining such as based on generalization, clustering based, spatial associations based, and approximations and aggregations  ...  Spatial data mining is a process of extracting expertise from large volumes of spatial data collected from different applications such as remote sensing, geographic systems and social networks, etc.  ...  A review of the basic concept of spatial data mining and its various commonly used methods in spatial data mining is discussed in this section [13] .  ... 
doi:10.35940/ijitee.l1136.10812s219 fatcat:zp546xjq4fge5l5czo2vhpys2u

Knowledge Discovery in Databases and Libraries
ENGLISH

Anil Kumar Dhiman
2011 DESIDOC Journal of Library & Information Technology  
So, intelligent tools for automated data mining and knowledge discovery are needed to deal with enormous data.  ...  As library and information centres are considered the backbone of knowledge organisation, knowledge discovery in databases (KDD) is also getting attention of library and information scientists.  ...  Hitherto, data mining tools mostly adopt techniques from statistics 10 , neural networks 11 , and visualisation 12 to classify data and extract patterns 13 .  ... 
doi:10.14429/djlit.31.6.1319 fatcat:cpjjesfgfbc6dndvlczmkhwabe

Data Mining: An AI Perspective

Xindong Wu
2004 The IEEE intelligent informatics bulletin  
Data mining, or knowledge discovery in databases (KDD), is an interdisciplinary area that integrates techniques from several fields including machine learning, statistics, and database systems, for the  ...  We discuss common topics in data mining and AI, including key AI ideas that have been used in both data mining and machine learning.  ...  CONCLUSION Knowledge discovery from large volumes of data is a research frontier for both data mining and AI, and has seen sustained research in recent years.  ... 
dblp:journals/cib/Wu04 fatcat:w575uedtkjgmrc7zpzr43bsunm

Evaluation of Customer Ratings on Restaurant by Clustering Techniques using R

Ankita Chopra, Dr. M. L. Saini
2019 Zenodo  
So, in this paper we try to collect data from a restaurant in Bangalore and evaluate its popularity based on ratings given by customers.  ...  In today's modern times food and lifestyle has become integral part of human system. People today aspire for good day at work and sumptuous and delicious food to eat at the end of the day.  ...  While data mining and knowledge discovery (or KDD) are frequently treated as synonyms, data mining is actually part of the knowledge discovery process.  ... 
doi:10.5281/zenodo.3262091 fatcat:ihckvuce2bgzbiulksk4rbqgsi

A Review Study of various Data Mining Classification and Clustering Techniques

Karambeer Kaur, Mr Surender Singh
2017 International Journal of Trend in Scientific Research and Development  
Data mining application includes a variety of methodologies that have been developed by commercial & research centers. This technique has been used for industrial, commercial and scientific purposes.  ...  The WEKA contains a set of visualization tools & algorithms for data analysis and predictive modeling, together with graphical user interfaces for simple access to this functionality.  ...  Data Mining (DM) represents a set of specific methods and algorithms aimed solely at extracting patterns from raw data. Data mining sometimes is also called knowledge discovery in databases (KDD).  ... 
doi:10.31142/ijtsrd135 fatcat:faftldo3tvdbtlmxuglcwcbzmy

Discovering spatio-social motifs of electoral support using discriminative pattern mining

Tomasz F. Stepinski, Josue Salazar, Wei Ding
2010 Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application - COM.Geo '10  
, Chapman & Hall/CRC, Data Mining and Knowledge Discovery Series, September, 2012. 91.  ...  Wu, "Mining Emerging Patterns by Streaming Feature Selection," The 18 th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 60-68, Beijing, China, August, 2012.  ... 
doi:10.1145/1823854.1823898 dblp:conf/comgeo/StepinskiSD10 fatcat:lt77jekqtzagtnemf7ll3mcryq

Discrimination Aware Data Mining in Internet of Things (IoT)

Asmita Gorave, Vrushali Kulkarni
2017 International Journal of Computer Applications  
However, it comes across problem of big data and extracting knowledge from such data using data mining techniques.  ...  This paper describes discrimination discovery and prevention issues faced by IoT. This paper also specifies the huge future research avenue related to discrimination aware data mining in IoT.  ...  Data mining is a technique to extract useful knowledge from raw data.  ... 
doi:10.5120/ijca2017912894 fatcat:5frrihdbova2dkrv4y2qqyyddm

Importance of Process Mining for Big Data Requirements Engineering

Sandhya Rani Kourla, Eesha Putti, Mina Maleki
2020 International Journal of Computer Science & Information Technology (IJCSIT)  
Data processing can be benefited by process mining, and in turn, helps to increase the productivity of the big data projects.  ...  Employing traditional data processing techniques lacks the invention of useful information because of the main characteristics of big data, including high volume, velocity, and variety.  ...  Knowledge discovery from big raw data is a process of collecting, cleaning, transforming, storing, applying mining algorithms, and discovering values from the raw data [13] .  ... 
doi:10.5121/ijcsit.2020.12401 fatcat:6fvruwovunfddh7fk22yeztjeq

Importance of Process Mining for Big Data Requirements Engineering

Sandhya Rani Kourla, Eesha Putti, Mina Maleki
2020 Zenodo  
Data processing can be benefited by process mining, and in turn, helps to increase the productivity of the big data projects.  ...  Employing traditional data processing techniques lacks the invention of useful information because of the main characteristics of big data, including high volume, velocity, and variety.  ...  Knowledge discovery from big raw data is a process of collecting, cleaning, transforming, storing, applying mining algorithms, and discovering values from the raw data [13] .  ... 
doi:10.5281/zenodo.4011576 fatcat:byk4mpi2cneynnu7osiu6xh5bq

A conversation with Professor Zhongzhi Shi

Zhongzhi Shi
2012 SIGKDD Explorations  
Knowledge Discovery from Data In 1989, Piatetsky-Shapiro and Fayyad presented the terminology "Knowledge Discovery from Databases".  ...  In 1996, the book "Advances in Knowledge Discovery and Data Mining" was published, and another book "Using the Data Warehouse" was published in China in 1994 helped promote the KDD research in China.  ... 
doi:10.1145/2207243.2207263 fatcat:woe3snawrrgbdkck27lwnza2r4

Knowledge Discovery in Protein Sequence Analysis Using Hierarchical Clustering Method

Desai Farhana
2021 International Journal of Current Research and Review  
Data Mining aims to discover hidden data from a large volume of data.  ...  Objective: Bioinformatics urges the need of discovering knowledge in the vast area of molecular biology by using data mining as the core.  ...  Many times Data Mining is also known as Knowledge Discovery, which means searching a large volume of data to discover patterns and new trends that go beyond simple analysis. 1 The Clustering technique  ... 
doi:10.31782/ijcrr.2021.131907 fatcat:byvgcel3anaivj2iq5vuz36fba

An introduction to SIGKDD and a reflection on the term 'data mining'

Gregory Piatetsky-Shapiro, Usama Fayyad
2012 SIGKDD Explorations  
and practice of predictive analytics, data science, Big Data, and Data Mining and Knowledge Discovery in Databases.  ...  SIGKDD also publishes SIGKDD Explorations -a magazine covering data mining and knowledge discovery.  ... 
doi:10.1145/2207243.2207269 fatcat:asvbve6mfzcvtinktga5zczt3y

Performance Analysis of Apriori Algorithm with Progressive Approach for Mining Data

Author Shilpa, Sunita Parashar
2011 International Journal of Computer Applications  
The Data Mining refers to extract or mine knowledge from huge volume of data. Association Rule mining is the technique for knowledge discovery.  ...  The Apriori algorithm is based upon candidate set generation and test method. The problem that always appears during mining frequent relations is its exponential complexity.  ...  Workshop on Knowledge Discovery and Data Mining, WKDD 2008, pp 607 -611, Jan. 2008. [3] R.Agrawal, T.Imielinski, and A.  ... 
doi:10.5120/3788-5216 fatcat:ncr3h3shcvdkvbhn5s4icdysy4

Knowledge Discovery from Static Datasets to Evolving Data Streams and Challenges

V. SiddaReddy, M. Narendra, K. Helini
2014 International Journal of Computer Applications  
Data steams can be considered a subfield of machine learning, data mining and knowledge discovery. Data Mining is a step in the process of knowledge discovery from large amount of data.  ...  In this research work, we will survey the main techniques and applications of data mining and data stream mining.  ...  Knowledge Discovery and Data Mining is an interdisciplinary area for extracting useful knowledge from large amount of data.  ... 
doi:10.5120/15284-3915 fatcat:ajhugnqnw5bzviqzhkjyfz4m4a
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