{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:3YKUDWKFT6UUJD5D65KGAZA4T5","short_pith_number":"pith:3YKUDWKF","canonical_record":{"source":{"id":"1701.04600","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-01-17T10:00:51Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"ba8a801a7eb6fd60af8d914479c58ddef859fd53262755f5731072633caddff6","abstract_canon_sha256":"9f4223ebc5625bea6c890f801b0259c05d0412ca0d6e531deb630d74300e0e5b"},"schema_version":"1.0"},"canonical_sha256":"de1541d9459fa9448fa3f75460641c9f4ab6d9ce296b7087180544daca2b86db","source":{"kind":"arxiv","id":"1701.04600","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1701.04600","created_at":"2026-05-18T00:52:43Z"},{"alias_kind":"arxiv_version","alias_value":"1701.04600v1","created_at":"2026-05-18T00:52:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1701.04600","created_at":"2026-05-18T00:52:43Z"},{"alias_kind":"pith_short_12","alias_value":"3YKUDWKFT6UU","created_at":"2026-05-18T12:30:58Z"},{"alias_kind":"pith_short_16","alias_value":"3YKUDWKFT6UUJD5D","created_at":"2026-05-18T12:30:58Z"},{"alias_kind":"pith_short_8","alias_value":"3YKUDWKF","created_at":"2026-05-18T12:30:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:3YKUDWKFT6UUJD5D65KGAZA4T5","target":"record","payload":{"canonical_record":{"source":{"id":"1701.04600","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-01-17T10:00:51Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"ba8a801a7eb6fd60af8d914479c58ddef859fd53262755f5731072633caddff6","abstract_canon_sha256":"9f4223ebc5625bea6c890f801b0259c05d0412ca0d6e531deb630d74300e0e5b"},"schema_version":"1.0"},"canonical_sha256":"de1541d9459fa9448fa3f75460641c9f4ab6d9ce296b7087180544daca2b86db","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:52:43.174914Z","signature_b64":"L1bJaIjEAocDmtGzMACIJAwMP34GzV6sp7lr7K8e/duF46E8JOzDwPFoby+hxkwHrNBQ0WrljVqg16EH2l3SCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"de1541d9459fa9448fa3f75460641c9f4ab6d9ce296b7087180544daca2b86db","last_reissued_at":"2026-05-18T00:52:43.174245Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:52:43.174245Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1701.04600","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":"2026-05-18T00:52:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Jrp6xnA60r6T3Zj8eq1ebrfFVAEaZULoXAOsRTEHXI3kfa+fvE4hzZGXx1zoChQd/53cmWzWXzEjnyX63Vr/Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-28T22:33:30.873209Z"},"content_sha256":"8b1a37bc84793fd8d7c2ff104db3f49ac18a8c0cc55efe1277c162ccaed13aaa","schema_version":"1.0","event_id":"sha256:8b1a37bc84793fd8d7c2ff104db3f49ac18a8c0cc55efe1277c162ccaed13aaa"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:3YKUDWKFT6UUJD5D65KGAZA4T5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Faster K-Means Cluster Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.LG","authors_text":"Amit Awekar, Siddhesh Khandelwal","submitted_at":"2017-01-17T10:00:51Z","abstract_excerpt":"There has been considerable work on improving popular clustering algorithm `K-means' in terms of mean squared error (MSE) and speed, both. However, most of the k-means variants tend to compute distance of each data point to each cluster centroid for every iteration. We propose a fast heuristic to overcome this bottleneck with only marginal increase in MSE. We observe that across all iterations of K-means, a data point changes its membership only among a small subset of clusters. Our heuristic predicts such clusters for each data point by looking at nearby clusters after the first iteration of "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.04600","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":"2026-05-18T00:52:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q8Tq/KEQ0c40StHcAb0BjXSPN/RpwT5G4KP4hC6/23AQWEnrdl3V4thf1RY+YdukErsc8+uUriXrnbbzavNlAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-28T22:33:30.873573Z"},"content_sha256":"71d248024267c34e5daf634a1b236e93b5519002bf8a47f8ed383fcb2b2baf2f","schema_version":"1.0","event_id":"sha256:71d248024267c34e5daf634a1b236e93b5519002bf8a47f8ed383fcb2b2baf2f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3YKUDWKFT6UUJD5D65KGAZA4T5/bundle.json","state_url":"https://pith.science/pith/3YKUDWKFT6UUJD5D65KGAZA4T5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3YKUDWKFT6UUJD5D65KGAZA4T5/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":"2026-06-28T22:33:30Z","links":{"resolver":"https://pith.science/pith/3YKUDWKFT6UUJD5D65KGAZA4T5","bundle":"https://pith.science/pith/3YKUDWKFT6UUJD5D65KGAZA4T5/bundle.json","state":"https://pith.science/pith/3YKUDWKFT6UUJD5D65KGAZA4T5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3YKUDWKFT6UUJD5D65KGAZA4T5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:3YKUDWKFT6UUJD5D65KGAZA4T5","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":"9f4223ebc5625bea6c890f801b0259c05d0412ca0d6e531deb630d74300e0e5b","cross_cats_sorted":["cs.IR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-01-17T10:00:51Z","title_canon_sha256":"ba8a801a7eb6fd60af8d914479c58ddef859fd53262755f5731072633caddff6"},"schema_version":"1.0","source":{"id":"1701.04600","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1701.04600","created_at":"2026-05-18T00:52:43Z"},{"alias_kind":"arxiv_version","alias_value":"1701.04600v1","created_at":"2026-05-18T00:52:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1701.04600","created_at":"2026-05-18T00:52:43Z"},{"alias_kind":"pith_short_12","alias_value":"3YKUDWKFT6UU","created_at":"2026-05-18T12:30:58Z"},{"alias_kind":"pith_short_16","alias_value":"3YKUDWKFT6UUJD5D","created_at":"2026-05-18T12:30:58Z"},{"alias_kind":"pith_short_8","alias_value":"3YKUDWKF","created_at":"2026-05-18T12:30:58Z"}],"graph_snapshots":[{"event_id":"sha256:71d248024267c34e5daf634a1b236e93b5519002bf8a47f8ed383fcb2b2baf2f","target":"graph","created_at":"2026-05-18T00:52:43Z","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":"There has been considerable work on improving popular clustering algorithm `K-means' in terms of mean squared error (MSE) and speed, both. However, most of the k-means variants tend to compute distance of each data point to each cluster centroid for every iteration. We propose a fast heuristic to overcome this bottleneck with only marginal increase in MSE. We observe that across all iterations of K-means, a data point changes its membership only among a small subset of clusters. Our heuristic predicts such clusters for each data point by looking at nearby clusters after the first iteration of ","authors_text":"Amit Awekar, Siddhesh Khandelwal","cross_cats":["cs.IR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-01-17T10:00:51Z","title":"Faster K-Means Cluster Estimation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.04600","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":"sha256:8b1a37bc84793fd8d7c2ff104db3f49ac18a8c0cc55efe1277c162ccaed13aaa","target":"record","created_at":"2026-05-18T00:52:43Z","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":"9f4223ebc5625bea6c890f801b0259c05d0412ca0d6e531deb630d74300e0e5b","cross_cats_sorted":["cs.IR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-01-17T10:00:51Z","title_canon_sha256":"ba8a801a7eb6fd60af8d914479c58ddef859fd53262755f5731072633caddff6"},"schema_version":"1.0","source":{"id":"1701.04600","kind":"arxiv","version":1}},"canonical_sha256":"de1541d9459fa9448fa3f75460641c9f4ab6d9ce296b7087180544daca2b86db","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"de1541d9459fa9448fa3f75460641c9f4ab6d9ce296b7087180544daca2b86db","first_computed_at":"2026-05-18T00:52:43.174245Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:52:43.174245Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"L1bJaIjEAocDmtGzMACIJAwMP34GzV6sp7lr7K8e/duF46E8JOzDwPFoby+hxkwHrNBQ0WrljVqg16EH2l3SCw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:52:43.174914Z","signed_message":"canonical_sha256_bytes"},"source_id":"1701.04600","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8b1a37bc84793fd8d7c2ff104db3f49ac18a8c0cc55efe1277c162ccaed13aaa","sha256:71d248024267c34e5daf634a1b236e93b5519002bf8a47f8ed383fcb2b2baf2f"],"state_sha256":"95845f592c8413da7f71b2448741a0a1246df822e4483d4813bcc293fd3f06e2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0+EY9B6a5eQFJRGgtJvRMdZCE30N6L7ZbqquE7NUpHss7YHXk3+vdBhXJrj+ustkXm+2sJ0bnTCxCPWPOyUYBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-28T22:33:30.875406Z","bundle_sha256":"f135bc207630a041db46ca53b6fcecc4fdf9892f9f8e99d99d87d14527bf25ac"}}