{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2012:ZAS2CNARHKNBFRZ3HWUZ6LV32A","short_pith_number":"pith:ZAS2CNAR","canonical_record":{"source":{"id":"1207.4958","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2012-07-11T11:11:54Z","cross_cats_sorted":[],"title_canon_sha256":"3b2237cc2c10af55027143124fc78017004d0589351e4de4362bb6e5713b0bdf","abstract_canon_sha256":"9db0db362eb2cb9c92f013a9c18908ff160ebfdb59d11d5c02fdda7b0161375c"},"schema_version":"1.0"},"canonical_sha256":"c825a134113a9a12c73b3da99f2ebbd000567cf9035e1d792e8f613de9e92db7","source":{"kind":"arxiv","id":"1207.4958","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1207.4958","created_at":"2026-05-18T03:50:34Z"},{"alias_kind":"arxiv_version","alias_value":"1207.4958v1","created_at":"2026-05-18T03:50:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1207.4958","created_at":"2026-05-18T03:50:34Z"},{"alias_kind":"pith_short_12","alias_value":"ZAS2CNARHKNB","created_at":"2026-05-18T12:27:30Z"},{"alias_kind":"pith_short_16","alias_value":"ZAS2CNARHKNBFRZ3","created_at":"2026-05-18T12:27:30Z"},{"alias_kind":"pith_short_8","alias_value":"ZAS2CNAR","created_at":"2026-05-18T12:27:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2012:ZAS2CNARHKNBFRZ3HWUZ6LV32A","target":"record","payload":{"canonical_record":{"source":{"id":"1207.4958","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2012-07-11T11:11:54Z","cross_cats_sorted":[],"title_canon_sha256":"3b2237cc2c10af55027143124fc78017004d0589351e4de4362bb6e5713b0bdf","abstract_canon_sha256":"9db0db362eb2cb9c92f013a9c18908ff160ebfdb59d11d5c02fdda7b0161375c"},"schema_version":"1.0"},"canonical_sha256":"c825a134113a9a12c73b3da99f2ebbd000567cf9035e1d792e8f613de9e92db7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:50:34.706288Z","signature_b64":"SYWKXM8LbDAK9NJ3WcYVyVKw2Z3Nu1ZRKiDSEeTRDNv30rQHEbwK8sDaX9LOkA3Yoeyoq6QF4xMeUhX/L9vpCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c825a134113a9a12c73b3da99f2ebbd000567cf9035e1d792e8f613de9e92db7","last_reissued_at":"2026-05-18T03:50:34.705733Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:50:34.705733Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1207.4958","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:50:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cf20N2d0afp/dkGtOkPHNcu2Dk9tYUB+Oemk/bmlMBr2f4ZDXiifOo9iYSXrGV7YKOlBpsAz0FA8vSV3/BsNDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T10:12:08.012130Z"},"content_sha256":"fc1b93e14a2472d7d4cc941b5ff44dabed91a20514793eebccbed12ad07bad19","schema_version":"1.0","event_id":"sha256:fc1b93e14a2472d7d4cc941b5ff44dabed91a20514793eebccbed12ad07bad19"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2012:ZAS2CNARHKNBFRZ3HWUZ6LV32A","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Minimally Infrequent Itemset Mining using Pattern-Growth Paradigm and Residual Trees","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Akshay Mittal, Arnab Bhattacharya, Ashish Gupta","submitted_at":"2012-07-11T11:11:54Z","abstract_excerpt":"Itemset mining has been an active area of research due to its successful application in various data mining scenarios including finding association rules. Though most of the past work has been on finding frequent itemsets, infrequent itemset mining has demonstrated its utility in web mining, bioinformatics and other fields. In this paper, we propose a new algorithm based on the pattern-growth paradigm to find minimally infrequent itemsets. A minimally infrequent itemset has no subset which is also infrequent. We also introduce the novel concept of residual trees. We further utilize the residua"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1207.4958","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:50:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"k8E2qaqLl/8YXoOWOIiwW5ff7LfBECratK8KHylc5XpB6IWEADi8bOxvdtTEri7zIgRvNCVCRVnUWab7LLrRAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T10:12:08.012488Z"},"content_sha256":"d802a1c707bf4fa6a003280c3a37199a5f17fc6da7af638c6f1834586227d68b","schema_version":"1.0","event_id":"sha256:d802a1c707bf4fa6a003280c3a37199a5f17fc6da7af638c6f1834586227d68b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZAS2CNARHKNBFRZ3HWUZ6LV32A/bundle.json","state_url":"https://pith.science/pith/ZAS2CNARHKNBFRZ3HWUZ6LV32A/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZAS2CNARHKNBFRZ3HWUZ6LV32A/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-04T10:12:08Z","links":{"resolver":"https://pith.science/pith/ZAS2CNARHKNBFRZ3HWUZ6LV32A","bundle":"https://pith.science/pith/ZAS2CNARHKNBFRZ3HWUZ6LV32A/bundle.json","state":"https://pith.science/pith/ZAS2CNARHKNBFRZ3HWUZ6LV32A/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZAS2CNARHKNBFRZ3HWUZ6LV32A/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2012:ZAS2CNARHKNBFRZ3HWUZ6LV32A","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"9db0db362eb2cb9c92f013a9c18908ff160ebfdb59d11d5c02fdda7b0161375c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2012-07-11T11:11:54Z","title_canon_sha256":"3b2237cc2c10af55027143124fc78017004d0589351e4de4362bb6e5713b0bdf"},"schema_version":"1.0","source":{"id":"1207.4958","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1207.4958","created_at":"2026-05-18T03:50:34Z"},{"alias_kind":"arxiv_version","alias_value":"1207.4958v1","created_at":"2026-05-18T03:50:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1207.4958","created_at":"2026-05-18T03:50:34Z"},{"alias_kind":"pith_short_12","alias_value":"ZAS2CNARHKNB","created_at":"2026-05-18T12:27:30Z"},{"alias_kind":"pith_short_16","alias_value":"ZAS2CNARHKNBFRZ3","created_at":"2026-05-18T12:27:30Z"},{"alias_kind":"pith_short_8","alias_value":"ZAS2CNAR","created_at":"2026-05-18T12:27:30Z"}],"graph_snapshots":[{"event_id":"sha256:d802a1c707bf4fa6a003280c3a37199a5f17fc6da7af638c6f1834586227d68b","target":"graph","created_at":"2026-05-18T03:50:34Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Itemset mining has been an active area of research due to its successful application in various data mining scenarios including finding association rules. Though most of the past work has been on finding frequent itemsets, infrequent itemset mining has demonstrated its utility in web mining, bioinformatics and other fields. In this paper, we propose a new algorithm based on the pattern-growth paradigm to find minimally infrequent itemsets. A minimally infrequent itemset has no subset which is also infrequent. We also introduce the novel concept of residual trees. We further utilize the residua","authors_text":"Akshay Mittal, Arnab Bhattacharya, Ashish Gupta","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2012-07-11T11:11:54Z","title":"Minimally Infrequent Itemset Mining using Pattern-Growth Paradigm and Residual Trees"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1207.4958","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:fc1b93e14a2472d7d4cc941b5ff44dabed91a20514793eebccbed12ad07bad19","target":"record","created_at":"2026-05-18T03:50:34Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"9db0db362eb2cb9c92f013a9c18908ff160ebfdb59d11d5c02fdda7b0161375c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2012-07-11T11:11:54Z","title_canon_sha256":"3b2237cc2c10af55027143124fc78017004d0589351e4de4362bb6e5713b0bdf"},"schema_version":"1.0","source":{"id":"1207.4958","kind":"arxiv","version":1}},"canonical_sha256":"c825a134113a9a12c73b3da99f2ebbd000567cf9035e1d792e8f613de9e92db7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c825a134113a9a12c73b3da99f2ebbd000567cf9035e1d792e8f613de9e92db7","first_computed_at":"2026-05-18T03:50:34.705733Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:50:34.705733Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SYWKXM8LbDAK9NJ3WcYVyVKw2Z3Nu1ZRKiDSEeTRDNv30rQHEbwK8sDaX9LOkA3Yoeyoq6QF4xMeUhX/L9vpCg==","signature_status":"signed_v1","signed_at":"2026-05-18T03:50:34.706288Z","signed_message":"canonical_sha256_bytes"},"source_id":"1207.4958","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fc1b93e14a2472d7d4cc941b5ff44dabed91a20514793eebccbed12ad07bad19","sha256:d802a1c707bf4fa6a003280c3a37199a5f17fc6da7af638c6f1834586227d68b"],"state_sha256":"d38a6a9d574d2f53b3366417ffbc9d577cc6eb927fda1064ecaa0e0bf8dce07c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"s5pkB7at7aWCpX6aMKHSZalGS7XDNv07d3GX8T2tBIJ6mKWkTJRMNbG+rvsxthBVyoqTpDNZf2YHtRZee2D/Dw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T10:12:08.014373Z","bundle_sha256":"2ac4939581cca1d120948ab373f60442419cf9b3c2469b2a220e2fe85764c6f9"}}