{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:L4MMBQZIT44UM5CYORPTWXE6V6","short_pith_number":"pith:L4MMBQZI","schema_version":"1.0","canonical_sha256":"5f18c0c3289f39467458745f3b5c9eafb0d49e1da088eded5a3b50e52d2ee77f","source":{"kind":"arxiv","id":"2410.06265","version":1},"attestation_state":"computed","paper":{"title":"SHADE: Deep Density-based Clustering","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Anna Beer, Christian B\\\"ohm, Claudia Plant, Collin Leiber, Lukas Miklautz, Pascal Weber, Walid Durani","submitted_at":"2024-10-08T18:03:35Z","abstract_excerpt":"Detecting arbitrarily shaped clusters in high-dimensional noisy data is challenging for current clustering methods. We introduce SHADE (Structure-preserving High-dimensional Analysis with Density-based Exploration), the first deep clustering algorithm that incorporates density-connectivity into its loss function. Similar to existing deep clustering algorithms, SHADE supports high-dimensional and large data sets with the expressive power of a deep autoencoder. In contrast to most existing deep clustering methods that rely on a centroid-based clustering objective, SHADE incorporates a novel loss"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2410.06265","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-08T18:03:35Z","cross_cats_sorted":[],"title_canon_sha256":"562d53c00b3d7c0a28bd9ef3b401365b27f962a205e522b983e098d7f4af5839","abstract_canon_sha256":"6f81012a03af0fd39a5f347db87cdf962e75ca9526a69f595809146ca6f119ea"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:17:59.060481Z","signature_b64":"5ttjLOtLWPJyUrPpYvV+Q/q2bxI1x3M+In5HOBkPXh3OTEDCVLaH/82AKW8z8+JELKwkzi8cIUpAGwoObcrDBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5f18c0c3289f39467458745f3b5c9eafb0d49e1da088eded5a3b50e52d2ee77f","last_reissued_at":"2026-07-05T09:17:59.060008Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:17:59.060008Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SHADE: Deep Density-based Clustering","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Anna Beer, Christian B\\\"ohm, Claudia Plant, Collin Leiber, Lukas Miklautz, Pascal Weber, Walid Durani","submitted_at":"2024-10-08T18:03:35Z","abstract_excerpt":"Detecting arbitrarily shaped clusters in high-dimensional noisy data is challenging for current clustering methods. We introduce SHADE (Structure-preserving High-dimensional Analysis with Density-based Exploration), the first deep clustering algorithm that incorporates density-connectivity into its loss function. Similar to existing deep clustering algorithms, SHADE supports high-dimensional and large data sets with the expressive power of a deep autoencoder. In contrast to most existing deep clustering methods that rely on a centroid-based clustering objective, SHADE incorporates a novel loss"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.06265","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2410.06265/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2410.06265","created_at":"2026-07-05T09:17:59.060065+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.06265v1","created_at":"2026-07-05T09:17:59.060065+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.06265","created_at":"2026-07-05T09:17:59.060065+00:00"},{"alias_kind":"pith_short_12","alias_value":"L4MMBQZIT44U","created_at":"2026-07-05T09:17:59.060065+00:00"},{"alias_kind":"pith_short_16","alias_value":"L4MMBQZIT44UM5CY","created_at":"2026-07-05T09:17:59.060065+00:00"},{"alias_kind":"pith_short_8","alias_value":"L4MMBQZI","created_at":"2026-07-05T09:17:59.060065+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/L4MMBQZIT44UM5CYORPTWXE6V6","json":"https://pith.science/pith/L4MMBQZIT44UM5CYORPTWXE6V6.json","graph_json":"https://pith.science/api/pith-number/L4MMBQZIT44UM5CYORPTWXE6V6/graph.json","events_json":"https://pith.science/api/pith-number/L4MMBQZIT44UM5CYORPTWXE6V6/events.json","paper":"https://pith.science/paper/L4MMBQZI"},"agent_actions":{"view_html":"https://pith.science/pith/L4MMBQZIT44UM5CYORPTWXE6V6","download_json":"https://pith.science/pith/L4MMBQZIT44UM5CYORPTWXE6V6.json","view_paper":"https://pith.science/paper/L4MMBQZI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.06265&json=true","fetch_graph":"https://pith.science/api/pith-number/L4MMBQZIT44UM5CYORPTWXE6V6/graph.json","fetch_events":"https://pith.science/api/pith-number/L4MMBQZIT44UM5CYORPTWXE6V6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L4MMBQZIT44UM5CYORPTWXE6V6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L4MMBQZIT44UM5CYORPTWXE6V6/action/storage_attestation","attest_author":"https://pith.science/pith/L4MMBQZIT44UM5CYORPTWXE6V6/action/author_attestation","sign_citation":"https://pith.science/pith/L4MMBQZIT44UM5CYORPTWXE6V6/action/citation_signature","submit_replication":"https://pith.science/pith/L4MMBQZIT44UM5CYORPTWXE6V6/action/replication_record"}},"created_at":"2026-07-05T09:17:59.060065+00:00","updated_at":"2026-07-05T09:17:59.060065+00:00"}