Bilişim dünyasına kaliteli, özgün ve Türkçe içerikler kazandırmayı hedefleyen bir platform..

Veri madenciliği, büyük ve karmaşık veri kümeleri içerisinden anlamlı bilgi, gizli örüntüler ve ilişkileri ortaya çıkarmayı amaçlayan; istatistik, makine öğrenmesi ve veri analizi tekniklerini bir araya getiren disiplinler arası bir süreçtir. Bu süreç sayesinde ham veriler, karar verme mekanizmalarını destekleyen anlamlı ve yorumlanabilir bilgilere dönüştürülür.

Veri madenciliği, büyük hacimli veri setlerinde daha önce bilinmeyen, potansiyel olarak faydalı ve yorumlanabilir örüntülerin keşfedilmesini amaçlayan; istatistiksel yöntemler, makine öğrenmesi algoritmaları ve veri tabanı tekniklerinin bütünleşik kullanımına dayanan bir bilgi keşfi sürecidir.

"Veri Madenciliği", veriler içerisinde yer alan faydalı fakat gizli kalmış anlamlı bilginin madenci sabrı ile çıkarılması sanatıdır (Teori ve Uygulamada Veri Madenciliği-Hidayet Takcı-Nobel Yayıncılık).

Veri Biliminde 9 Mesafe Ölçütü

Business Information: Data mining goals to define Business goals for identifying Project plan for producing Your situation to assess Data Processing: Data Collection Data-Describing Data Exploring Quality Verification Data Readiness: Data Selection Data-Cleaning Constructing output Data Integrating And also Data Formatting Data Modelling: Techniques selection Tests design Models build And also in Models assess Data Evaluation: Results evaluation Process review Determining Data Deployment: Deployment Planning Final results Reporting And also in Final results Reviewing Significant Methods in Data Mining Association rule mining Anomaly detection Clustering Classification Regression Characterization Cluster analysis Evolution analysis Association and also Correlation Analysis Also in Prediction Data Mining with other Domains Image analysis Signal Processing Bioinformatics Business Web Technology Computer Graphics Pattern Recognition Spatial Data Analysis And also in Information Retrieval Important Applications of Data Mining Classification of DNA sequences Voice recognition Natural Language Processing Market Analysis Production Control Fraud Detection Customer Retention And also in Science Exploration Recent Research Topics Distributed data mining architecture in sporadic wireless sensor network using reliable and also intelligent protocol Data mining approaches also for distributed intrusion detection system in cloud computing based on toward a policy Medical time series data using mining closed weighted sequential patterns also with flexing time interval Modern technologies also using educational portal with data mining support. Data mining also for predictive models of economic systems Incremental data mining also using constraint based filtering Entrepreneurship analysis also for data mining applications Data mining approach also for analysis of soil behaviour and prediction of crop yield Spatial data mining platform also with easySDM Network data mining also using NOESIS open source framework Data mining applied also based on parallel optimized preparation(POP) Mapping crime areas in the Czech republic also using proposal of web data mining application Weighted FP-Tree mining algorithms also for fast processing of conversion time data flow in cloud computing Medical data mining also with apache spark using empirical comparison of three ensemble methods. The judgement of poisoning cased also using application of data mining technology Mining bike sharing system data also with the usage of characterising and predicting urban mobility dynamics The prefix span algorithm applied on sequential pattern mining also based on hotspot data in riau province