Abstract: System pre-processes and computes class distribution of decision attribute and statistics for discretization of continuous attributes through use of compute buckets. System computes the variability of each of the attributes and considers only the non-zero variability attributes. System computes the discernibility strength of each attribute. The software system generates size 1 patterns using compute bucket and calculates if each pattern of size 1 is a reliable pattern for any class. The system calculates if reliable pattern of size 1 is a significant pattern for any class. The system generates size k patterns from size k?1 patterns checking for significance of size k patterns and refinability. The system readjusts pattern statistics for only significant patterns for size k?1 patterns. The system computes a cumulative coverage of the sorted relevant patterns of up to size k by finding out the union of records of that particular class.

Abstract: The software system processes extracts reliable, significant and relevant patterns. System runs through preprocessing steps. System then generates the size 1 patterns. It then checks for both reliability and refinability of the size 1 patterns. System grows the refinable patterns by increasing the attributes and its values in the pattern by one at a time to find a size 2 pattern. The system then uses the number of pattern occurrences of size 2 pattern as a basis to find the reliable patterns. System also checks for statistical significance over the size 1 patterns and once again for the refinability of the size 2 patterns. System checks for relevance of the size 1 patterns by obtaining the disjointed record complement set. Software system readjusts the pattern statistics of size 1 and removes the non-relevant super-patterns. This process is repeated from size 2 to N.

Abstract: The software system discretises continuous attributes by initially sorting attribute value pairs. The system creates partitions based on unique attribute index. The computing system adds attribute value records to the partition element until minimum frequency has been reached. The system calculates whether new partition element is mutually insignificant compared with each of partition elements in a bucket. The system adds the mutually insignificant partition element into the bucket; otherwise, the system closes the bucket and creates a new bucket to add the mutually significant partition element to the new bucket. After all the buckets have been created, the system groups buckets of continuous attributes in to a bucket group so that the buckets in a group are mutually insignificant. If bucket is mutually significant, the system creates a new bucket group and adds subsequent mutually significant bucket to said new bucket group.

Abstract: The software system discretises continuous attributes by initially sorting attribute value pairs. The system creates partitions based on unique attribute index. The computing system adds attribute value records to the partition element until minimum frequency has been reached. The system calculates whether new partition element is mutually insignificant compared with each of partition elements in a bucket. The system adds the mutually insignificant partition element into the bucket; otherwise, the system closes the bucket and creates a new bucket to add the mutually significant partition element to the new bucket. After all the buckets have been created, the system groups buckets of continuous attributes in to a bucket group so that the buckets in a group are mutually insignificant. If bucket is mutually significant, the system creates a new bucket group and adds subsequent mutually significant bucket to said new bucket group.

Abstract: System pre-processes and computes class distribution of decision attribute and statistics for discretization of continuous attributes through use of compute buckets. System computes the variability of each of the attributes and considers only the non-zero variability attributes. System computes the discernibility strength of each attribute. The software system generates size 1 patterns using compute bucket and calculates if each pattern of size 1 is a reliable pattern for any class. The system calculates if reliable pattern of size 1 is a significant pattern for any class. The system generates size k patterns from size k?1 patterns checking for significance of size k patterns and refinability. The system readjusts pattern statistics for only significant patterns for size k?1 patterns. The system computes a cumulative coverage of the sorted relevant patterns of up to size k by finding out the union of records of that particular class.

Abstract: The software system processes extracts reliable, significant and relevant patterns. System runs through preprocessing steps. System then generates the size 1 patterns. It then checks for both reliability and refinability of the size 1 patterns. System grows the refinable patterns by increasing the attributes and its values in the pattern by one at a time to find a size 2 pattern. The system then uses the number of pattern occurrences of size 2 pattern as a basis to find the reliable patterns. System also checks for statistical significance over the size 1 patterns and once again for the refinability of the size 2 patterns. System checks for relevance of the size 1 patterns by obtaining the disjointed record complement set. Software system readjusts the pattern statistics of size 1 and removes the non-relevant super-patterns. This process is repeated from size 2 to N.