2018 LATEST DATA MINING PROJECTS


Data mining is mining knowledge from data, Involving methods at the intersection of machine learning, statistics, and database systems. Its the powerful new technology with great potential to help companies focus on the most important information in their data warehouses. We have the best in class infrastructure, lab set up , Training facilities, And experienced research and development team for both educational and corporate sectors.
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IEEE 2017-18 web mining/Data mining projects.

This section consists of projects related to data mining 2018 IEEE project list, classes on Data Mining with explanations and examples, Real time experience is available, latest ideas with enhancement for Latest IEEE papers on Data mining . We provide abstract and complete explanation on synopsis. All the latest IEEE projects are available on Data Mining, titles and abstracts can be download from our website.

TED001 POINT-OF-INTEREST RECOMMENDATION FOR LOCATION PROMOTION IN LOCATION-BASED SOCIAL NETWORKS ABSTRACT
TED002 NETSPAM: A NETWORK-BASED SPAM DETECTION FRAMEWORK FOR REVIEWS IN ONLINE SOCIAL MEDIA ABSTRACT
TED003 SOCIALQ&A: AN ONLINE SOCIAL NETWORK BASED QUESTION AND ANSWER SYSTEM ABSTRACT
TED004 MODELING URBAN BEHAVIOR BY MINING GEOTAGGED SOCIAL DATA ABSTRACT
TED005 A WORKFLOW MANAGEMENT SYSTEM FOR SCALABLE DATA MINING ON CLOUDS ABSTRACT
TED006 FIDOOP: PARALLEL MINING OF FREQUENT ITEMSETS USING MAPREDUCE ABSTRACT
TED007 SENTIMENT ANALYSIS OF TOP COLLEGES IN INDIA USING TWITTER DATA ABSTRACT
TED008 FRAPPE: DETECTING MALICIOUS FACEBOOK APPLICATIONS ABSTRACT
TED009 AUTOMATICALLY MINING FACETS FOR QUERIES FROM THEIR SEARCH RESULTS ABSTRACT
TED010 CYBERBULLYING DETECTION BASED ON SEMANTIC-ENHANCED MARGINALIZED DENOISING AUTO-ENCODER ABSTRACT
TED011 CROWDSOURCING FOR TOP-K QUERY PROCESSING OVER UNCERTAIN DATA ABSTRACT
TED012 EFFICIENT ALGORITHMS FOR MINING TOP-K HIGH UTILITY ITEMSETS ABSTRACT
TED013 RATING PREDICTION BASED ON SOCIAL SENTIMENT FROM TEXTUAL REVIEWS ABSTRACT
TED014 QUANTIFYING POLITICAL LEANING FROM TWEETS, RETWEETS, AND RETWEETERS ABSTRACT
TED015 PRACTICAL APPROXIMATE K NEAREST NEIGHBOR QUERIES WITH LOCATION AND QUERY PRIVACY ABSTRACT
TED016 A META-TOP-DOWN METHOD FOR LARGE-SCALE HIERARCHICAL CLASSIFICATION ABSTRACT
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TECHNOFISTis the best project institute in Bangalore for carrying out final year projects on Data Mining Domain. 8th sem computer science engineering students and information science students can call our head office situated in R.T.Nagar for Data Mining project explanation.
Genuine lab set up is obtainable for students to practice codes and implementation. A project lead will be fixed to each batch of cse and ise students to carry out final year engineering projects on Data Mining. M-Tech. BE students are requested to send a mail or contact our branches at the first, soon after exams and complete the projects in vacations to keep away from last minute rush.

In technofist, With good lab set up, Infrastructure and with Experienced faculties are accessible round the clock to guide final year computer science and information science students to complete Data Mining or machine learning projects on time. Technofist is one of the most outstanding institute for Data Mining 2017 IEEE project implementation. Several of Data Mining projects with IEEE papers are available with us. We provide Online support using teamviewer , skype will be accessible for out station students as well as out of country students.

IEEE 2017-18 WEB MINING/DATA MINING BASED PROJECTS

Data mining is the process of searching huge amount of data from different aspects and summarize it to useful information. Data mining is logical than physical subset. Our concerns usually implicate mining and text based classification on Data mining projects for Students.

The usages of variety of tools associated to data analysis for identifying relationships in data are the process for data mining. Our concern support data mining projects for IT and CSE students to carry out their academic research projects.

Hadoop framework includes following Modules:

  • Hadoop MapReduce
  • Hadoop Distributed File System (HDFS™)
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Data mining is the process of searching huge amount of data from different aspects and summarize it to useful information. Data mining is logical than physical subset. Our concerns usually implicate mining and text based classification on Data mining projects for Students.

The usages of variety of tools associated to data analysis for identifying relationships in data are the process for data mining. Our concern support data mining projects for IT and CSE students to carry out their academic research projects.

Technics used for Data Mining

  • Anomaly detection (outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors that require further investigation.
  • Association rule learning (dependency modelling) – Searches for relationships between variables. For example, a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis.
  • Clustering – is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data.
  • Classification – is the task of generalizing known structure to apply to new data. For example, an e-mail program might attempt to classify an e-mail as "legitimate" or as "spam".
  • Regression – attempts to find a function which models the data with the least error that is, for estimating the relationships among data or datasets.
  • Summarization – providing a more compact representation of the data set, including visualization and report generation.

Data Mining Operations

  • Link Analysis links between individuals rather than characterising whole
  • Predictive Modelling (supervised learning) use observations to learn to predict
  • Database Segmentation (unsupervised learning) partition data into similar groups.