Oracle Data Miner [BEST] Download

Data mining requires a problem definition, collection and cleansing of data, and model building. Much of the time spent in a typical data mining project is devoted to understanding and processing of data.

Oracle Database 12c Release 1 (12.1) Enterprise Edition (includes Oracle Database Personal Edition) or an earlier version of Oracle Database Enterprise Edition is the server. In addition to the database, Oracle Data Miner requires the installation of a Data Miner repository account. The repository is a separate account in the database named ODMRSYS. This repository is shared by all user accounts in the database that have been granted the appropriate privileges to use the Data Miner repository.

Oracle Data Miner Download

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Insert snippet: To insert a snippet into your code in a SQL Worksheet or in a PL/SQL function or procedure, drag the snippet from the snippets window and drop it into the desired place in your code. Then edit the syntax so that the SQL function is valid in the current context. To see a brief description of a SQL function in a tool tip, hold the pointer over the function name. Oracle Data Miner provides snippets for the EXPLAIN, PREDICT, and PROFILE procedures in DBMS_PREDICTIVE_ANALYTICS and for the Data Mining functions for scoring data using prediction, clustering, or feature extraction.

To grant rights to another account, define a database connection for the account and open the account in the Data Miner navigator. The GUI tells you that the account does not have the correct grants. Click OK for the grants to be created. You must log in using an administrative (SYS) account.

After you download a patch or a new version or you update the database that you connect to, you are notified that upgrade is necessary when you open a connection used for data mining. The GUI issues a message describing the problem and asks you if it should perform necessary migration. If you answer yes, you are prompted for the administrative (SYS) password.

Either of these conditions is detected when you open the connection in the Data Miner Navigator. If the ODMRSYS default permanent tablespace is not an ASM tablespace, then a dialog is displayed requesting an ASM tablespace. The ASM tablespace does not replace the existing default permanent tablespace already specified for ODMRSYS, but instead, it converts the workflow_data column that stores the workflow XML data in the ODMR$_WORKFLOWS table. The workflow_data is usually the largest data component stored in ODMRSYS. This approach reduces the amount of time required to perform the migration.

Using Oracle Data Miner 4.0: This tutorial covers the use of Oracle Data Miner 4.0 to perform data mining on Oracle Database 12c. In this lesson, you examine and solve a data mining business problem by using the Oracle Data Miner graphical user interface (GUI). The Oracle Data Miner GUI is included as an extension of Oracle SQL Developer, version 4.0.

Sample Data is loaded in your account when you install Oracle Data Miner. You also have access to sample data in SH and other schemas. See Oracle Data Miner Installation and Administration Guide for information about how to add tables and views from SH and other schemas to you account.

Oracle Data Miner documentation is included in the Oracle Database Documentation Library for the version of the database that you have installed. Documentation Libraries are posted at the Documentation site at in the Database section. To go directly to the Business Intelligence and Data Warehousing documentation, use =db112&id=dwbitab if you connect to Oracle Database 11g Release 2 (11.2) or =db121&id=dwbitab if you connect to Oracle database 12c Release1 (12.1).

Oracle Data Miner is an extension to Oracle SQL Developer that enables data scientists and business and data analysts to view data, rapidly build multiple machine learning models, compare and evaluate multiple models, apply them to new data, and accelerate model deployment.

Oracle Data Mining provides a powerful, state-of-the-art data mining capability within Oracle Database. You can use Oracle Data Mining to build and deploy predictive and descriptive data mining applications, to add intelligent capabilities to existing applications, and to generate predictive queries for data exploration.

Oracle Data Mining offers a broad set of in-database algorithms for performing a variety of machine learning tasks, such as classification, regression, anomaly detection, feature extraction, clustering, and market basket analysis. The algorithms can work on standard case data, transactional data, star schemas, and unstructured text data. Oracle Data Mining is uniquely suited to the analysis of very large data sets.

Oracle Data Mining is implemented in the Oracle Database kernel. Data Mining models are first class database objects. Oracle Data Mining processes use built-in features of Oracle Database to maximize scalability and make efficient use of system resources.

No Data Movement: Some data mining products require that the data be exported from a corporate database and converted to a specialized format for mining. With Oracle Data Mining, no data movement or conversion is needed. This makes the entire mining process less complex, time-consuming, and error-prone, and it allows for the mining of very large data sets.

Security: Your data is protected by the extensive security mechanisms of Oracle Database. Moreover, specific database privileges are needed for different data mining activities. Only users with the appropriate privileges can define, manipulate, or apply mining model objects.

Data Preparation and Administration: Most data must be cleansed, filtered, normalized, sampled, and transformed in various ways before it can be mined. Up to 80% of the effort in a data mining project is often devoted to data preparation. Oracle Data Mining can automatically manage key steps in the data preparation process. Additionally, Oracle Database provides extensive administrative tools for preparing and managing data.

Ease of Data Refresh: Mining processes within Oracle Database have ready access to refreshed data. Oracle Data Mining can easily deliver mining results based on current data, thereby maximizing its timeliness and relevance.

Oracle Database Analytics: Oracle Database offers many features for advanced analytics and business intelligence. Oracle Data Mining can easily be integrated with other analytical features of the database, such as statistical analysis and OLAP.

Domain Environment: Data mining models have to be built, tested, validated, managed, and deployed in their appropriate application domain environments. Data mining results may need to be post-processed as part of domain specific computations (for example, calculating estimated risks and response probabilities) and then stored into permanent repositories or data warehouses. With Oracle Data Mining, the pre- and post-mining activities can all be accomplished within the same environment.

Scoring refers to the process of applying a data mining model to data to generate predictions. The scoring process may require significant system resources. Vast amounts of data may be involved, and algorithmic processing may be very complex.

Oracle Exadata Storage Servers combine Oracle's smart storage software and Oracle's industry-standard hardware to deliver the industry's highest database storage performance. For more information about Oracle Exadata, visit the Oracle Technology Network.

Oracle Data Mining supports building of a persistent Oracle Data Mining partitioned model. A partitioned model organizes and represents multiple models as partitions in a single model entity, enabling a user to easily build and manage models tailored to independent slices of data. Persistent means that the partitioned model has an on-disk representation. The product manages the organization of the partitioned model and simplifies the process of scoring the partitioned model. You must include the partition columns as part of the USING clause when scoring.

The Oracle Data Mining PL/SQL API is implemented in the DBMS_DATA_MINING PL/SQL package, which contains routines for building, testing, and maintaining data mining models. A batch apply operation is also included in this package.

The following example shows part of a simple PL/SQL script for creating an SVM classification model called SVMC_SH_Clas_sample. The model build uses weights, specified in a weights table, and settings, specified in a settings table. The weights influence the weighting of target classes. The settings override default behavior. The model uses Automatic Data Preparation (prep_auto_on setting). The model is trained on the data in mining_data_build_v.

The following example shows a query that applies the classification model svmc_sh_clas_sample to the data in the view mining_data_apply_v. The query returns the average age of customers who are likely to use an affinity card. The results are broken out by gender.

Oracle Data Miner uses a work flow paradigm to capture, document, and automate the process of building, evaluating, and applying data mining models. Within a work flow, you can specify data transformations, build and evaluate multiple models, and score multiple data sets. You can then save work flows and share them with other users.

Predictive analytics uses data mining technology, but knowledge of data mining is not needed to use predictive analytics. You can use predictive analytics simply by specifying an operation to perform on your data. You do not need to create or use mining models or understand the mining techniques and algorithms summarized in "Oracle Data Mining Basics ".

The possibilities for combining different analytics are virtually limitless. Example 2-3 shows data mining and text processing within a single SQL query. The query selects all customers who have a high propensity to attrite (> 80% chance), are valuable customers (customer value rating > 90), and have had a recent conversation with customer services regarding a Checking Plus account. The propensity to attrite information is computed using a Data Mining model called tree_model. The query uses the Oracle Text CONTAINS operator to search call center notes for references to Checking Plus accounts. ff782bc1db