By Longbing Cao
In the current thriving international economic climate a necessity has advanced for advanced facts research to augment an organization’s construction structures, decision-making strategies, and function. In flip, information mining has emerged as probably the most energetic components in info applied sciences. Domain pushed facts Mining deals state-of the-art examine and improvement results on methodologies, thoughts, methods and profitable functions in area pushed, actionable wisdom discovery.
About this book:
- Enhances the actionability and wider deployment of latest data-centered facts mining via a mixture of area and company orientated components, constraints and intelligence.
- Examines real-world demanding situations to and complexities of the present KDD methodologies and techniques.
- Details a paradigm shift from "data-centered development mining" to "domain pushed actionable wisdom discovery" for next-generation KDD study and functions.
- Bridges the space among enterprise expectancies and study output via targeted exploration of the findings, concepts and classes discovered in carrying out numerous large-scale, real-world info mining enterprise applications
- Includes concepts, methodologies and case stories in real-life company information mining
- Addresses new components reminiscent of weblog mining
Domain pushed info Mining is acceptable for researchers, practitioners and college scholars within the parts of knowledge mining and information discovery, wisdom engineering, human-computer interplay, synthetic intelligence, clever details processing, choice aid platforms, wisdom administration, and KDD undertaking management.
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Extra resources for Domain Driven Data Mining
Example text
Human-Mining-Cooperated Traditional data mining is an automated trial and error process. Deliverables are presumed as predefined automated algorithms and tools. It is arguable that such automated methodology has both strengths and weaknesses. The positive side is that it makes the user’s life easy. However, it confronts challenges in aspects such as lacking capability in involving domain intelligence and adapting to dynamic situations in the business world. In particular, automated data mining encounters big trouble in handling the dynamics and ad-hoc requests widely seen in enterprise data mining applications.
It is arguable that such automated methodology has both strengths and weaknesses. The positive side is that it makes the user’s life easy. However, it confronts challenges in aspects such as lacking capability in involving domain intelligence and adapting to dynamic situations in the business world. In particular, automated data mining encounters big trouble in handling the dynamics and ad-hoc requests widely seen in enterprise data mining applications. Actionable knowledge discovery in a constrained context determines that realworld data mining is more likely to be human involved rather than automated.
In-depth patterns’ (or ‘deep patterns’) are not straightforward such as frequency-based, but can only be discovered through more powerful models following thorough data and business understanding and effectively involving domain intelligence or expert guidance. An example is to mine for insider trading patterns in capital markets. Without deep understanding of the business and data, a naive approach is to analyze the price movement change in data partitions of pre-event, event and post-event. A deeper pattern analysis on such price difference analysis may be considered by involving domain factors such as considering market or limit orders, market impact, and checking the performance of potential abnormal return, liquidity, volatility and correlation.