Congress,[x] and a new mood in Britain for rapid decolonisation in India.[y][41][44] Bose's legacy is mixed. Among many in India, he is seen as a hero, his saga serving as a would-be counterpoise to the many actions of regeneration, negotiation, and reconciliation over a quarter-century through which the independence of India was achieved.[z][aa][ab] His collaborations with Japanese Fascism and Nazism pose serious ethical dilemmas,[ac] especially his reluctance to publicly criticize the worst excesses of German anti-Semitism from 1938 onwards or to offer refuge in India to its victims. Chittaranjan Das, a voice for aggressive nationalism in Bengal. In 1923, Bose was elected the President of Indian Youth Congress and also the Secretary of the Bengal State Congress. He became the editor of the newspaper "Forward", which had been founded by Chittaranjan Das.[81] Bose worked as the CEO of the Calcutta Municipal Corporation for Das when the latter was elected mayor of Calcutta in 1924.[82] During the same year, when Bose was leading a protest march in Calcutta, he, Maghfoor Ahmad Ajazi and other leaders were arrested and imprisoned.[83][failed verification] After a roundup of nationalists in 1925, Bose was sent to prison in Mandalay, British Burma, where he contracted tuberculosis.[84] Subhas Bose (in military uniform) with Congress president, Motilal Nehru taking the salute. Annual meeting, Indian National Congress, 29 December 1928 In 1927, after being released from prison, Bose became general secretary of the Congress party and worked with Jawaharlal Nehru for independence. In late December 1928, Bose organised the Annual Meeting of the Indian National Congress in Calcutta.[85] His most memorable role was as General officer commanding (GOC) Congress Volunteer Corps.[85] Author Nirad Chaudhuri wrote about the meeting: Bose organized a volunteer corps in uniform, its officers were even provided with steel-cut epaulettes ... his uniform was made by a firm of British tailors in Calcutta, Harman's. A telegram addressed to him as GOC was delivered to the British General in Fort William and was the subject of a good deal of malicious gossip in the (British Indian) press. Mahatma Gandhi as a sincere pacifist vowed to non-violence, did not like the strutting, clicking of boots, and saluting, and he afterward described the Calcutta session of the Congress as a Bertram Mills circus, which caused a great deal of indignation among the Bengalis.[85] A little later, Bose was again arrested and jailed for civil disobedience; this time he emerged to become Mayor of Calcutta in 1930.[84] Chittaranjan Das, a voice for aggressive nationalism in Bengal. In 1923, Bose was elected the President of Indian Youth Congress and also the Secretary of the Bengal State Congress. He became the editor of the newspaper "Forward", which had been founded by Chittaranjan Das.[81] Bose worked as the CEO of the Calcutta Municipal Corporation for Das when the latter was elected ma

1. DATA MINING
PRESENTED BY:
PROFESSOR VIJAY KHATA
COMPUTER DEPARTMENT

2. DATA MINING TASK PRIMITIVES
• Data mining task is represented in form of data mining query.
• This data mining query is defined in terms of data mining task primitives.
• Will allow the user to interactively communicate with the data mining system.
• Basic building block or components that are used to construct a data mining process.
• The use of data mining task primitives can provide a modular and reusable approach,
which can improve performance, efficiency, and understandability of the data mining
process.

3. DATA MINING TASK PRIMITIVES
• The data mining task primitives are as follows:
1. The set of task relevant data to be mined
2. Kind of knowledge to be mined
3. Background knowledge to be used in the discovery process
4. Interestingness measures and thresholds for pattern evaluation
5. Representation for visualizing the discovered pattern

4. 1. TASK RELEVANT DATA
 this specifies the portions of the database or the set of data in which user is interested.
 Database name
Database tables
Relevant attributes
Data grouping criteria

5. 2. THE KIND OF KNOWELDGE TO BE MINED.
This specifies the data mining functions can be performed, such as:
1. Characterization
2. Discrimination
3. Association or correlation analysis
4. Classification
5. Prediction
6. clustering

6. 3. BACKGROUNG KNOWLEDGE TO BE USED IN THE
DISCOVERY PROCESS.
It refers to any prior information or understanding that is used to guide the data mining
process.
This can include domain-specific knowledge, such as industry-specific terminology,
trends or best practices, as well as knowledge about data itself.
The use of background knowledge can help to improve the accuracy and relevance of
the insights obtained from the data mining process.
Concept hierarchies are a popular form of background knowledge, which allows data to
be mined at multiple level of abstraction.

7. 4. Interestingness measures and thresholds for pattern evaluation
• This is used to evaluate the patterns that are discovered by the process of knowledge
discovery.
• These measures are used to identify patterns that are meaningful or relevant to the task.

8. 5. Representation for visualizing the discovered pattern
It refers to the methods used to represent the patterns or insights discovered through
data mining in a way that is easy to understand and interpret.
Visualization techniques such as charts, graphs, and maps are commonly used to
represent the data and can help to highlight important trends, patterns, or relationships
within the data.

9. INTEGRATION OF DATA MINING SYSTEM WITH
DATABASE
The data mining system is integrated with a database or data warehouse system so that it
can do its task in an effective presence.
A data mining system operates in an environment that needed it to communicate with
other data systems like database system.
There are possible integration schemes that can integrate these systems.

10. 1. NO COUPLING
No coupling defines that a data mining system will not use any function of a database
or data warehouse system.
It can retrieve data from specific source (including file system), process data using some
data mining algorithms, and therefore save the mining results in a different file.

11. 2. LOOSE COUPLING
Loose coupling means that a data mining system will use some facilities of a database or
data warehouse system, fetching data from repository managed by these system,
performing data mining, and then storing the mining results either in a file or in a
designated place in a database or data warehouse.

12. 3. SEMI-TIGHT COUPLING-ENHANCED DATA
MINING PERFORMANCE
The semi-tight coupling means that besides linking a data mining system to a
Database/Data warehouse system, efficient implementations of a few essential data
mining primitives (sorting, indexing, aggregation) can be provided in the Database/Data
Warehouse system.

13. 4. TIGHT COUPLING-A UNIFORM INFORMATION
PROCESSING ENVIRONMENT
Tight coupling means that a Data Mining system is smoothly integrated into
the database/Data warehouse system.