Data Mining with big data total ieee project and entire files.
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The document discusses the revolution of data mining in the context of high-dimensional data, emphasizing the rapid growth of data from various sources. It introduces the hace theorem, which highlights key characteristics of big data, including its vastness, autonomy, and complexity. The paper also proposes a data processing model for effectively analyzing big data from a mining perspective.
Data Mining with big data total ieee project and entire files.
- 1. ABSTRACT Data mining Revolution on High Dimensional Data Data mining involves exploring and analyzing large amounts of data to find patterns for big data. Data volumes grow exponentially. Its growth is caused by the increasing number of systems and people acting as data sources of textual, verbal, video and transactional information. This data contains insider information and patterns previously hidden due to lack of proper technologies. Generally, the goal of the data mining is either classification or prediction. In classification, the idea is to sort data into groups. For example, a marketer might be interested in the characteristics of those who responded versus who didn’t respond to a promotion. Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. Our HACE theorem suggests that the key characteristics of the Big Data are 1) huge with heterogeneous and diverse data sources, 2) Autonomous with distributed and decentralized control, and 3) complex and evolving in data and knowledge associations.