By Mark F. Hornick
No matter if you're a software program developer, structures architect, facts analyst, or enterprise analyst, for you to reap the benefits of info mining within the improvement of complicated analytic purposes, Java info Mining, JDM, the recent usual now carried out in center DBMS and knowledge mining/analysis software program, is a key resolution part. This ebook is the fundamental consultant to the use of the JDM typical interface, written by way of individuals to the JDM typical. The publication discusses and illustrates tips to remedy actual difficulties utilizing the JDM API.
The authors offer you with:
* information mining introduction-an evaluate of information mining and the issues it could possibly tackle throughout industries JDMs position in strategic suggestions to info mining-related problems
* JDM essentials-concepts, layout strategy and layout matters, with designated code examples in Java an internet prone interface to let JDM performance in an SOA surroundings and representation of JDM XML Schema for JDM objects
* JDM in practice-the use of JDM from seller implementations and techniques to purchaser functions, integration, and utilization effect of knowledge mining on IT infrastructure a how-to advisor for construction purposes that use the JDM API.
* information mining introduction-an review of information mining and the issues it may handle throughout industries JDMs position in strategic suggestions to info mining-related problems
* JDM essentials-concepts, layout process and layout matters, with specified code examples in Java an online prone interface to permit JDM performance in an SOA surroundings and representation of JDM XML Schema for JDM objects
* JDM in practice-the use of JDM from seller implementations and ways to shopper functions, integration, and utilization effect of information mining on IT infrastructure a how-to advisor for development functions that use the JDM API.
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Additional resources for Java Data Mining Strategy, Standard, and Practice A Practical Guide for architecture, design, and implementation
Figure 2-2 illustrates a pattern in minutes of usage that a possible churner may exhibit before terminating his account. However, different groups of individuals may be exhibiting this behavior, for example, teenage girls with large family and friend circles, 30-something single male professionals, etc. Understanding the particular characteristics of each of these groups enables businesses to develop campaigns to retain such customers or to increase their service usage. Data mining can identify the important factors or attributes that lead to a specific behavior, as well as group individuals according to their behavior.
This can be in the form of, for example, rules that define customer profiles, common co-occurrences of product sales enabling cross-sell, or a representative case that describes a set of patients susceptible to a type of cancer. Zinc is added in dust form to the de-areated solution, which is drawn under pressure through a filter press; which causes the gold and zinc to precipitate onto canvas (heavy cloth) filter leaves. This zinc-gold precipitate (condensed into a solid) is then cleaned from the filters while extreme heat burns off the zinc.
This section takes this metaphor to its limit by contrasting a description of the gold mining process [Wells 2006] with data mining. Gold mining involves the science, technology, and business of the discovery of gold, in addition to its removal and sale in the marketplace. Gold may be found in many places, most commonly rock but even sea water; in very small quantities. More often it is found in greater quantities in veins associated with igneous rocks, rocks created by heat such as quartzite. “Data Mining” is somewhat of a misnomer since we are not trying to discover “data,” but the knowledge that is present in data.